commit bd1bd52c6c9d7c90fec661f86f9e38acea7c5b77
Author: DanielS <daniel.schumacher@caspos.de>
Date:   Fri May 29 00:47:49 2026 +0200

    initial commit

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diff --git a/app/main.py b/app/main.py
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--- /dev/null
+++ b/app/main.py
@@ -0,0 +1,523 @@
+import os
+import sys
+import json
+import base64
+import subprocess
+from io import BytesIO
+import numpy as np
+from PIL import Image, ImageDraw
+import cv2
+import albumentations as A
+import ctypes
+import time
+import socket
+import threading
+
+from fastapi import FastAPI, HTTPException, BackgroundTasks
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse, JSONResponse
+from pydantic import BaseModel
+
+from inference import FruitVegetableDetector, CLASSES
+
+app = FastAPI(title="POS Object Detection API", description="Fruit & Vegetable checkout detection microservice")
+
+# Initialize detector
+detector = FruitVegetableDetector()
+
+# Paths
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+STATUS_FILE = os.path.join(BASE_DIR, "training_status.json")
+
+# Data Models
+class DetectionRequest(BaseModel):
+    image: str  # Base64 encoded image or Data URL
+
+class TrainRequest(BaseModel):
+    epochs: int = 15
+    batch_size: int = 8
+    lr: float = 0.001
+
+class CaptureRequest(BaseModel):
+    image: str
+    class_name: str
+
+class SaveArticlesRequest(BaseModel):
+    articles: dict
+
+class ScanRequest(BaseModel):
+    class_name: str
+
+# Serve frontend static files
+static_path = os.path.join(os.path.dirname(__file__), "static")
+os.makedirs(static_path, exist_ok=True)
+
+# Helper function to read training status
+def get_training_status():
+    if not os.path.exists(STATUS_FILE):
+        return {
+            "status": "idle",
+            "epoch": 0,
+            "total_epochs": 0,
+            "train_loss": [],
+            "val_loss": [],
+            "progress": 0.0,
+            "message": "No training history found."
+        }
+    try:
+        with open(STATUS_FILE, "r") as f:
+            return json.load(f)
+    except Exception:
+        return {"status": "error", "message": "Failed to read status file."}
+
+# Helper to write status
+def write_training_status(status_dict):
+    with open(STATUS_FILE, "w") as f:
+        json.dump(status_dict, f, indent=2)
+
+@app.post("/api/detect")
+async def detect_objects(payload: DetectionRequest):
+    """
+    Accepts base64 encoded frame, runs ONNX inference, and returns detections.
+    """
+    try:
+        data_url = payload.image
+        if "," in data_url:
+            data_url = data_url.split(",")[1]
+            
+        img_bytes = base64.b64decode(data_url)
+        img = Image.open(BytesIO(img_bytes)).convert("RGB")
+        img_np = np.array(img)
+        
+        predictions = detector.detect(img_np)
+        
+        return {
+            "status": "success",
+            "predictions": predictions
+        }
+    except Exception as e:
+        return JSONResponse(
+            status_code=400,
+            content={"status": "error", "message": f"Inference failed: {str(e)}"}
+        )
+
+def bg_generate_dataset():
+    try:
+        from data_generator import build_dataset
+        build_dataset(base_dir=os.path.join(BASE_DIR, "dataset"), train_count=200, val_count=50)
+        write_training_status({
+            "status": "idle",
+            "epoch": 0,
+            "total_epochs": 0,
+            "train_loss": [],
+            "val_loss": [],
+            "progress": 100.0,
+            "message": "Dataset generation completed successfully!"
+        })
+    except Exception as e:
+        write_training_status({
+            "status": "failed",
+            "epoch": 0,
+            "total_epochs": 0,
+            "train_loss": [],
+            "val_loss": [],
+            "progress": 0.0,
+            "message": f"Dataset generation failed: {str(e)}"
+        })
+
+@app.post("/api/generate_dataset")
+async def trigger_dataset_generation(background_tasks: BackgroundTasks):
+    """
+    Triggers dataset generation asynchronously.
+    """
+    status = get_training_status()
+    if status.get("status") in ["training", "generating_data"]:
+        raise HTTPException(status_code=400, detail="An operation is already in progress.")
+        
+    write_training_status({
+        "status": "generating_data",
+        "epoch": 0,
+        "total_epochs": 0,
+        "train_loss": [],
+        "val_loss": [],
+        "progress": 10.0,
+        "message": "Starting synthetic data generation process..."
+    })
+    
+    background_tasks.add_task(bg_generate_dataset)
+    
+    return {"status": "success", "message": "Dataset generation started in the background."}
+
+@app.post("/api/train")
+async def trigger_training(req: TrainRequest):
+    """
+    Triggers model training.
+    """
+    status = get_training_status()
+    if status.get("status") in ["training", "generating_data"]:
+        raise HTTPException(status_code=400, detail="Training or data generation is already in progress.")
+        
+    write_training_status({
+        "status": "training",
+        "epoch": 0,
+        "total_epochs": req.epochs,
+        "train_loss": [],
+        "val_loss": [],
+        "progress": 5.0,
+        "message": "Initializing training background process..."
+    })
+    
+    # Launch training process in background
+    cmd = [
+        sys.executable, 
+        os.path.join(BASE_DIR, "train.py"),
+        "--epochs", str(req.epochs),
+        "--batch-size", str(req.batch_size),
+        "--lr", str(req.lr)
+    ]
+    subprocess.Popen(cmd, cwd=BASE_DIR)
+    
+    return {"status": "success", "message": f"Training initiated for {req.epochs} epochs."}
+
+@app.get("/api/train_status")
+async def get_status():
+    """
+    Returns current training status.
+    """
+    # Check if background processes are actually running
+    # If status says training/generating but no process matches, we can update status to failed/idle.
+    status = get_training_status()
+    return status
+
+@app.post("/api/export")
+async def trigger_export():
+    """
+    Exports trained PyTorch weights to ONNX format and reloads the detector.
+    """
+    pytorch_path = os.path.join(BASE_DIR, "models", "model.pt")
+    if not os.path.exists(pytorch_path):
+        raise HTTPException(status_code=404, detail="No trained PyTorch weights found at models/model.pt. Train a model first.")
+        
+    try:
+        # Run export script synchronously since it is fast (few seconds)
+        cmd = [sys.executable, os.path.join(BASE_DIR, "export.py")]
+        result = subprocess.run(cmd, cwd=BASE_DIR, capture_output=True, text=True, check=True)
+        
+        # Reload the detector model
+        detector.load_model()
+        
+        return {
+            "status": "success", 
+            "message": "Model successfully exported to ONNX and loaded into memory.",
+            "log": result.stdout
+        }
+    except Exception as e:
+        return JSONResponse(
+            status_code=500,
+            content={"status": "error", "message": f"ONNX Export failed: {str(e)}"}
+        )
+
+@app.get("/api/augmented_preview")
+async def get_augmented_preview():
+    """
+    Generates a sample preview image from the generator, applies augmentations,
+    draws bounding boxes on both, and returns them side-by-side.
+    """
+    try:
+        from data_generator import generate_sample
+        
+        # Generate a raw sample image and its YOLO annotations
+        img, annotations = generate_sample(width=320, height=320)
+        img_np = np.array(img)
+        
+        # Setup the same augmentation pipeline used in training
+        transform = A.Compose([
+            A.ShiftScaleRotate(shift_limit=0.1, scale_limit=0.15, rotate_limit=30, p=1.0),
+            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=1.0),
+            A.GaussNoise(p=1.0)
+        ], bbox_params=A.BboxParams(format='pascal_voc', label_fields=['category_ids']))
+        
+        # Parse annotations to box absolute coords
+        boxes = []
+        classes = []
+        for ann in annotations:
+            parts = ann.strip().split()
+            if len(parts) == 5:
+                class_id = int(parts[0])
+                xc, yc, w, h = map(float, parts[1:])
+                xmin = (xc - w/2) * 320
+                ymin = (yc - h/2) * 320
+                xmax = (xc + w/2) * 320
+                ymax = (yc + h/2) * 320
+                boxes.append([xmin, ymin, xmax, ymax])
+                classes.append(class_id)
+                
+        boxes = np.array(boxes, dtype=np.float32).reshape(-1, 4)
+        
+        # Create augmented image
+        augmented_np = img_np.copy()
+        aug_boxes = boxes.copy()
+        if len(boxes) > 0:
+            try:
+                augmented = transform(image=img_np, bboxes=boxes, category_ids=classes)
+                augmented_np = augmented["image"]
+                aug_boxes = np.array(augmented["bboxes"], dtype=np.float32).reshape(-1, 4)
+            except Exception:
+                pass
+                
+        # Draw bounding boxes on original image
+        orig_draw = img.copy()
+        draw_tool = ImageDraw.Draw(orig_draw)
+        for idx, box in enumerate(boxes):
+            draw_tool.rectangle(box.tolist(), outline=(255, 0, 0), width=3)
+            draw_tool.text((box[0]+2, box[1]+2), CLASSES[classes[idx]], fill=(255, 0, 0))
+            
+        # Draw bounding boxes on augmented image
+        aug_img = Image.fromarray(augmented_np)
+        draw_tool_aug = ImageDraw.Draw(aug_img)
+        for idx, box in enumerate(aug_boxes):
+            draw_tool_aug.rectangle(box.tolist(), outline=(0, 255, 0), width=3)
+            draw_tool_aug.text((box[0]+2, box[1]+2), CLASSES[classes[idx]], fill=(0, 255, 0))
+            
+        # Create side-by-side preview canvas
+        preview = Image.new("RGB", (640, 320))
+        preview.paste(orig_draw, (0, 0))
+        preview.paste(aug_img, (320, 0))
+        
+        # Buffer and return as base64
+        buffered = BytesIO()
+        preview.save(buffered, format="JPEG")
+        preview_base64 = base64.b64encode(buffered.getvalue()).decode()
+        
+        return {
+            "status": "success",
+            "image": f"data:image/jpeg;base64,{preview_base64}"
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Failed to generate preview: {str(e)}")
+
+@app.get("/api/simulated_frame")
+async def get_simulated_frame():
+    """
+    Generates a raw synthetic scale frame (without annotations) for client-side POS testing.
+    """
+    try:
+        from data_generator import generate_sample
+        # Generate raw sample image (640x480)
+        img, _ = generate_sample(width=640, height=480)
+        
+        buffered = BytesIO()
+        img.save(buffered, format="JPEG")
+        img_base64 = base64.b64encode(buffered.getvalue()).decode()
+        
+        return {
+            "status": "success",
+            "image": f"data:image/jpeg;base64,{img_base64}"
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Failed to generate simulated frame: {str(e)}")
+
+@app.post("/api/capture_training_image")
+async def capture_training_image(payload: CaptureRequest):
+    """
+    Saves a webcam snapshot and generates a centered YOLO format bounding box label.
+    """
+    try:
+        class_name = payload.class_name
+        if class_name not in CLASSES:
+            raise HTTPException(status_code=400, detail=f"Ungültige Produktklasse: {class_name}")
+            
+        class_idx = CLASSES.index(class_name)
+        
+        # Decode base64
+        data_url = payload.image
+        if "," in data_url:
+            data_url = data_url.split(",")[1]
+        img_bytes = base64.b64decode(data_url)
+        img = Image.open(BytesIO(img_bytes)).convert("RGB")
+        
+        # Save image with unique filename
+        import time
+        timestamp = int(time.time() * 1000)
+        filename = f"user_capture_{timestamp}.jpg"
+        
+        images_dir = os.path.join(BASE_DIR, "dataset", "images", "train")
+        labels_dir = os.path.join(BASE_DIR, "dataset", "labels", "train")
+        os.makedirs(images_dir, exist_ok=True)
+        os.makedirs(labels_dir, exist_ok=True)
+        
+        img.save(os.path.join(images_dir, filename), quality=95)
+        
+        # Write YOLO label centered at scale
+        # Bounding box: class_idx, x_center=0.5, y_center=0.5, width=0.6, height=0.6
+        label_filename = f"user_capture_{timestamp}.txt"
+        with open(os.path.join(labels_dir, label_filename), "w") as f:
+            f.write(f"{class_idx} 0.500000 0.500000 0.650000 0.650000\n")
+            
+        # Count total user captured images
+        user_images = [f for f in os.listdir(images_dir) if f.startswith("user_capture_")]
+        count = len(user_images)
+        
+        return {
+            "status": "success",
+            "message": f"Bild erfolgreich als Training für {class_name} erfasst!",
+            "count": count
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Fehler bei Bild-Erfassung: {str(e)}")
+
+@app.get("/api/capture_count")
+async def get_capture_count():
+    """
+    Returns the number of user captured training images.
+    """
+    images_dir = os.path.join(BASE_DIR, "dataset", "images", "train")
+    if not os.path.exists(images_dir):
+        return {"count": 0}
+    user_images = [f for f in os.listdir(images_dir) if f.startswith("user_capture_")]
+    return {"count": len(user_images)}
+
+# --- BARCODE SCANNER EMULATION (Windows Keyboard Hook) ---
+KEYEVENTF_KEYUP = 0x0002
+VK_RETURN = 0x0D
+ARTICLES_FILE = os.path.join(BASE_DIR, "articles.json")
+
+def simulate_keyboard_type(text: str):
+    """
+    Types the EAN code and presses Enter globally on Windows.
+    Uses hardware scan codes mapped from virtual key codes for highest compatibility.
+    """
+    try:
+        # Give a small 200ms delay to allow cashier window to gain focus if clicked manually
+        time.sleep(0.2)
+        
+        KEYEVENTF_KEYUP = 0x0002
+        MAPVK_VK_TO_VSC = 0
+        
+        for char in text:
+            vk = None
+            if '0' <= char <= '9':
+                vk = 0x30 + int(char)
+            elif 'A' <= char <= 'Z':
+                vk = ord(char)
+            elif 'a' <= char <= 'z':
+                vk = ord(char.upper())
+                
+            if vk is not None:
+                # Map to hardware scan code
+                scan_code = ctypes.windll.user32.MapVirtualKeyW(vk, MAPVK_VK_TO_VSC)
+                
+                # Key Down
+                ctypes.windll.user32.keybd_event(vk, scan_code, 0, 0)
+                time.sleep(0.001)
+                
+                # Key Up
+                ctypes.windll.user32.keybd_event(vk, scan_code, KEYEVENTF_KEYUP, 0)
+                time.sleep(0.001)
+                
+        # Send VK_RETURN (Enter) with scan code
+        vk_ret = 0x0D
+        scan_ret = ctypes.windll.user32.MapVirtualKeyW(vk_ret, MAPVK_VK_TO_VSC)
+        
+        ctypes.windll.user32.keybd_event(vk_ret, scan_ret, 0, 0)
+        time.sleep(0.001)
+        ctypes.windll.user32.keybd_event(vk_ret, scan_ret, KEYEVENTF_KEYUP, 0)
+        
+        return True
+    except Exception as e:
+        print(f"Failed to type keyboard input: {e}", file=sys.stderr)
+        return False
+
+@app.get("/api/articles")
+async def get_articles():
+    if not os.path.exists(ARTICLES_FILE):
+        return {}
+    try:
+        with open(ARTICLES_FILE, "r") as f:
+            return json.load(f)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Fehler beim Laden der Artikeldatenbank: {str(e)}")
+
+@app.post("/api/articles")
+async def save_articles(payload: SaveArticlesRequest):
+    try:
+        with open(ARTICLES_FILE, "w") as f:
+            json.dump(payload.articles, f, indent=2)
+        from data_generator import reload_classes
+        reload_classes()
+        return {"status": "success", "message": "Artikeldatenbank erfolgreich gespeichert!"}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Fehler beim Speichern der Artikeldatenbank: {str(e)}")
+
+# --- TCP SOCKET CLIENT SENDER ---
+TCP_PORT = 9000
+
+def send_ean_via_tcp(ean_code: str):
+    """
+    Sends the EAN code to the cash register over TCP on port 9000.
+    Since the cash register is the server (listening on port 9000), we connect
+    as a client, transmit the barcode, and close the connection.
+    """
+    message = (ean_code + "\r\n").encode('utf-8')
+    
+    print(f"TCP Client Mode: Connecting to cash register at 127.0.0.1:{TCP_PORT}...", flush=True)
+    try:
+        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+            s.settimeout(0.5) # Fast timeout
+            s.connect(('127.0.0.1', TCP_PORT))
+            s.sendall(message)
+            print(f"TCP Client: Successfully sent EAN {ean_code} to cash register.", flush=True)
+            return True
+    except Exception as e:
+        print(f"TCP Client: Connection to cash register failed (is register listening on port {TCP_PORT}?): {e}", flush=True)
+        return False
+
+@app.post("/api/scan_ean")
+async def scan_ean_endpoint(payload: ScanRequest):
+    try:
+        class_name = payload.class_name
+        if not os.path.exists(ARTICLES_FILE):
+            raise HTTPException(status_code=404, detail="Artikeldatenbank existiert nicht.")
+            
+        with open(ARTICLES_FILE, "r") as f:
+            articles = json.load(f)
+            
+        if class_name not in articles:
+            raise HTTPException(status_code=404, detail=f"Klasse {class_name} nicht in Artikeldatenbank.")
+            
+        ean_code = articles[class_name].get("ean", "")
+        if not ean_code:
+            raise HTTPException(status_code=400, detail=f"Kein EAN-Code für {class_name} hinterlegt.")
+            
+        # 1. Send via TCP (Direct socket integration)
+        tcp_sent = send_ean_via_tcp(ean_code)
+        
+        # 2. Parallel keyboard emulation fallback on Windows
+        keyboard_sent = simulate_keyboard_type(ean_code)
+        
+        if tcp_sent or keyboard_sent:
+            methods = []
+            if tcp_sent:
+                methods.append("TCP")
+            if keyboard_sent:
+                methods.append("Tastatur")
+            method_desc = " + ".join(methods)
+            
+            return {
+                "status": "success",
+                "class": class_name,
+                "name": articles[class_name].get("name", class_name),
+                "ean": ean_code,
+                "message": f"Barcode {ean_code} gesendet via {method_desc}!"
+            }
+        else:
+            raise HTTPException(status_code=500, detail="Übertragung fehlgeschlagen (TCP und Tastaturemulation gescheitert).")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# Mount static files (must be after api definitions)
+app.mount("/", StaticFiles(directory=static_path, html=True), name="static")
+
+@app.get("/")
+async def root():
+    return FileResponse(os.path.join(static_path, "index.html"))
diff --git a/app/static/app.js b/app/static/app.js
new file mode 100644
index 0000000..5c37b26
--- /dev/null
+++ b/app/static/app.js
@@ -0,0 +1,1025 @@
+// Application Configuration
+const PRICING_DATABASE = {
+    "apfel_elstar": { name: "Apfel Elstar", price: 2.99, unit: "kg" },
+    "apfel_gala": { name: "Apfel Gala", price: 2.49, unit: "kg" },
+    "apfel_granny_smith": { name: "Apfel Granny Smith", price: 2.79, unit: "kg" },
+    "banane_chiquita": { name: "Banane Chiquita", price: 1.99, unit: "kg" },
+    "banane_bio": { name: "Bio Banane", price: 2.29, unit: "kg" },
+    "birne": { name: "Birne Abate Fetel", price: 3.29, unit: "kg" },
+    "orange": { name: "Orangen", price: 2.19, unit: "kg" },
+    "zitrone": { name: "Zitronen", price: 0.59, unit: "Stk." },
+    "limette": { name: "Limetten", price: 0.49, unit: "Stk." },
+    "erdbeere": { name: "Erdbeeren Schale", price: 2.99, unit: "Stk." },
+    "blaubeere": { name: "Kulturheidelbeeren", price: 1.99, unit: "Stk." },
+    "weintraube_hell": { name: "Tafeltrauben hell", price: 3.99, unit: "kg" },
+    "weintraube_dunkel": { name: "Tafeltrauben dunkel", price: 4.29, unit: "kg" },
+    "pfirsich": { name: "Pfirsiche", price: 2.99, unit: "kg" },
+    "tomate": { name: "Rispentomaten", price: 3.49, unit: "kg" },
+    "gurke": { name: "Salatgurke", price: 0.99, unit: "Stk." },
+    "kartoffel": { name: "Speisekartoffeln", price: 1.49, unit: "kg" },
+    "karotte": { name: "Speisemöhren", price: 1.29, unit: "kg" },
+    "zwiebel_gelb": { name: "Speisezwiebeln", price: 1.19, unit: "kg" },
+    "zwiebel_rot": { name: "Rote Zwiebeln", price: 1.69, unit: "kg" },
+    "knoblauch": { name: "Knoblauch", price: 0.89, unit: "Stk." },
+    "brokkoli": { name: "Brokkoli", price: 1.79, unit: "Stk." },
+    "paprika_rot": { name: "Paprika rot", price: 3.99, unit: "kg" },
+    "paprika_gelb": { name: "Paprika gelb", price: 3.99, unit: "kg" },
+    "paprika_gruen": { name: "Paprika grün", price: 3.49, unit: "kg" },
+    "champignon": { name: "Champignons weiß", price: 1.89, unit: "Stk." },
+    "zucchini": { name: "Zucchini", price: 2.19, unit: "kg" },
+    "avocado": { name: "Avocado Hass", price: 1.29, unit: "Stk." }
+};
+
+// Application State
+let activeTab = 'checkout';
+let isWebcamActive = false;
+let webcamStream = null;
+let simulatedStreamInterval = null;
+let currentFrameBase64 = null;
+let isDetecting = false;
+let autoDetectEnabled = true;
+let trainingPollInterval = null;
+
+// Scanner Emulator State
+let autoScanEnabled = true;
+let stableClass = null;
+let stableCount = 0;
+const STABILITY_THRESHOLD = 4; // number of frames (approx 0.8s)
+let lastScannedClass = null;
+let scanLockActive = false;
+let articlesDatabase = {};
+
+// Cart State
+let shoppingCart = [];
+
+// DOM Elements
+const videoEl = document.getElementById("webcam");
+const canvasEl = document.getElementById("camera-canvas");
+const ctx = canvasEl.getContext("2d");
+const toggleCameraBtn = document.getElementById("toggle-camera-btn");
+const triggerScaleBtn = document.getElementById("trigger-scale-btn");
+const autoDetectToggle = document.getElementById("auto-detect-toggle");
+const cameraTypeBadge = document.getElementById("camera-type-badge");
+const quickSelectButtons = document.getElementById("quick-selection-buttons");
+const cartList = document.getElementById("cart-list");
+const cartSubtotalEl = document.getElementById("cart-subtotal");
+const cartTotalEl = document.getElementById("cart-total");
+const emptyCartMsg = document.getElementById("empty-cart-msg");
+
+// --- TAB NAVIGATION ---
+function switchTab(tabName) {
+    activeTab = tabName;
+    document.querySelectorAll('.tab-content').forEach(el => el.classList.remove('active'));
+    document.querySelectorAll('.nav-btn').forEach(el => el.classList.remove('active'));
+    
+    document.getElementById(`tab-${tabName}`).classList.add('active');
+    document.getElementById(`tab-btn-${tabName}`).classList.add('active');
+    
+    if (tabName === 'dashboard') {
+        // Start polling training status when entering dashboard
+        startTrainingPolling();
+        refreshAugmentedPreview();
+    } else {
+        stopTrainingPolling();
+    }
+
+    if (tabName === 'articles') {
+        loadArticlesDatabase();
+    }
+}
+
+// --- CAMERA & DETECTION LOGIC ---
+async function startWebcam() {
+    try {
+        webcamStream = await navigator.mediaDevices.getUserMedia({
+            video: { width: 640, height: 480, facingMode: "environment" }
+        });
+        videoEl.srcObject = webcamStream;
+        videoEl.style.display = "none";
+        isWebcamActive = true;
+        cameraTypeBadge.textContent = "LIVE-WEBCAM";
+        cameraTypeBadge.style.background = "var(--color-primary)";
+        toggleCameraBtn.innerHTML = `
+            <svg width="18" height="18" viewBox="0 0 24 24" stroke="currentColor" fill="none" stroke-width="2"><rect x="3" y="3" width="18" height="18" rx="2"/><circle cx="9" cy="9" r="2"/></svg>
+            Kamera deaktivieren
+        `;
+        document.getElementById("stream-status").textContent = "LIVE";
+        document.getElementById("stream-status").classList.add("live");
+        
+        // Stop simulated stream if active
+        if (simulatedStreamInterval) {
+            clearInterval(simulatedStreamInterval);
+            simulatedStreamInterval = null;
+        }
+        
+        requestAnimationFrame(processWebcamFrame);
+    } catch (err) {
+        console.error("Webcam access denied/unavailable:", err);
+        alert("Webcam konnte nicht gestartet werden. Der simulierte Kamera-Modus wird verwendet.");
+        startSimulatedStream();
+    }
+}
+
+function stopWebcam() {
+    if (webcamStream) {
+        webcamStream.getTracks().forEach(track => track.stop());
+        webcamStream = null;
+    }
+    videoEl.srcObject = null;
+    videoEl.style.display = "none";
+    isWebcamActive = false;
+    toggleCameraBtn.innerHTML = `
+        <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><path d="M23 19a2 2 0 0 1-2 2H3a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h4l2-3h6l2 3h4a2 2 0 0 1 2 2z"/><circle cx="12" cy="13" r="4"/></svg>
+        Webcam aktivieren
+    `;
+    startSimulatedStream();
+}
+
+function processWebcamFrame() {
+    if (!isWebcamActive) return;
+    
+    // Draw current video frame to canvas
+    ctx.drawImage(videoEl, 0, 0, canvasEl.width, canvasEl.height);
+    
+    // Get frame as base64
+    currentFrameBase64 = canvasEl.toDataURL("image/jpeg", 0.7);
+    
+    // Auto-detect if enabled
+    if (autoDetectEnabled && !isDetecting) {
+        runDetection();
+    }
+    
+    setTimeout(() => {
+        requestAnimationFrame(processWebcamFrame);
+    }, 150); // Limit detection frequency for performance
+}
+
+function startSimulatedStream() {
+    cameraTypeBadge.textContent = "SIMULATIONS-MODUS";
+    cameraTypeBadge.style.background = "var(--color-bg-dark)";
+    document.getElementById("stream-status").textContent = "Simuliert";
+    document.getElementById("stream-status").classList.add("live");
+    
+    const loadSimulatedFrame = async () => {
+        try {
+            const response = await fetch("/api/simulated_frame");
+            const data = await response.json();
+            if (data.status === "success" && data.image) {
+                const img = new Image();
+                img.onload = () => {
+                    ctx.drawImage(img, 0, 0, canvasEl.width, canvasEl.height);
+                    currentFrameBase64 = data.image;
+                    
+                    if (autoDetectEnabled && !isDetecting) {
+                        runDetection();
+                    }
+                };
+                img.src = data.image;
+            }
+        } catch (err) {
+            console.error("Failed to load simulated frame:", err);
+        }
+    };
+    
+    loadSimulatedFrame(); // First load immediately
+    simulatedStreamInterval = setInterval(loadSimulatedFrame, 2500); // cycle frames every 2.5s
+}
+
+// Perform detection API request
+async function runDetection() {
+    if (!currentFrameBase64) return;
+    isDetecting = true;
+    
+    try {
+        const response = await fetch("/api/detect", {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ image: currentFrameBase64 })
+        });
+        const data = await response.json();
+        
+        if (data.status === "success") {
+            drawDetections(data.predictions);
+            updateQuickButtons(data.predictions);
+            processStabilityAndScan(data.predictions);
+        }
+    } catch (err) {
+        console.error("Detection error:", err);
+    } finally {
+        isDetecting = false;
+    }
+}
+
+// Draw bounding boxes on canvas
+function drawDetections(predictions) {
+    // Redraw the base frame first (to clear old boxes)
+    const img = new Image();
+    img.onload = () => {
+        ctx.drawImage(img, 0, 0, canvasEl.width, canvasEl.height);
+        
+        // Draw boxes
+        predictions.forEach(pred => {
+            const [xmin, ymin, xmax, ymax] = pred.box;
+            const w = xmax - xmin;
+            const h = ymax - ymin;
+            const isHighConf = pred.confidence > 0.70;
+            
+            // Box outline
+            ctx.lineWidth = 3;
+            ctx.strokeStyle = isHighConf ? "#10b981" : "#c084fc"; // Green for high conf, purple for low conf
+            ctx.strokeRect(xmin, ymin, w, h);
+            
+            // Label box background
+            ctx.fillStyle = isHighConf ? "rgba(16, 185, 129, 0.85)" : "rgba(168, 85, 247, 0.85)";
+            const text = `${PRICING_DATABASE[pred.class]?.name || pred.class} (${(pred.confidence * 100).toFixed(0)}%)`;
+            ctx.font = "bold 12px sans-serif";
+            const textWidth = ctx.measureText(text).width;
+            ctx.fillRect(xmin - 1, ymin - 22, textWidth + 12, 22);
+            
+            // Label text
+            ctx.fillStyle = "#ffffff";
+            ctx.fillText(text, xmin + 5, ymin - 6);
+        });
+    };
+    img.src = currentFrameBase64;
+}
+
+// Update cashier recommendation buttons
+function updateQuickButtons(predictions) {
+    quickSelectButtons.innerHTML = "";
+    
+    // Filter predictions with confidence > 0.10
+    const candidates = predictions.filter(p => p.confidence > 0.10);
+    
+    if (candidates.length === 0) {
+        quickSelectButtons.innerHTML = `
+            <div class="empty-state">
+                <p>Keine Objekte erkannt</p>
+                <span>Confidence Schwellenwert unter 10%</span>
+            </div>
+        `;
+        return;
+    }
+    
+    candidates.forEach(pred => {
+        const itemInfo = PRICING_DATABASE[pred.class] || { name: pred.class, price: 1.0, unit: "kg" };
+        const confPercent = (pred.confidence * 100).toFixed(0);
+        const isHigh = pred.confidence > 0.70;
+        
+        const btn = document.createElement("button");
+        btn.className = `quick-btn ${isHigh ? 'high-conf' : 'low-conf'}`;
+        btn.onclick = () => sendEanToPos(pred.class);
+        
+        btn.innerHTML = `
+            <span class="btn-label">${itemInfo.name}</span>
+            <span class="btn-conf">${confPercent}% Conf</span>
+        `;
+        quickSelectButtons.appendChild(btn);
+    });
+}
+
+// --- SHOPPING CART LOGIC ---
+function addToCart(classKey) {
+    const itemInfo = PRICING_DATABASE[classKey];
+    if (!itemInfo) return;
+    
+    // Simulate weight or piece count
+    let quantity, itemTotal;
+    if (itemInfo.unit === "kg") {
+        // Random weight between 0.150kg and 1.800kg
+        quantity = parseFloat((0.150 + Math.random() * 1.65).toFixed(3));
+        itemTotal = parseFloat((quantity * itemInfo.price).toFixed(2));
+    } else {
+        quantity = 1;
+        itemTotal = itemInfo.price;
+    }
+    
+    const cartItem = {
+        key: classKey,
+        name: itemInfo.name,
+        pricePerUnit: itemInfo.price,
+        unit: itemInfo.unit,
+        quantity: quantity,
+        total: itemTotal,
+        timestamp: Date.now()
+    };
+    
+    shoppingCart.push(cartItem);
+    updateCartUI();
+    
+    // Scale animation feedback
+    const scaleCard = document.querySelector(".camera-card");
+    scaleCard.classList.add("scale-flash");
+    setTimeout(() => scaleCard.classList.remove("scale-flash"), 400);
+}
+
+function deleteCartItem(timestamp) {
+    shoppingCart = shoppingCart.filter(item => item.timestamp !== timestamp);
+    updateCartUI();
+}
+
+function clearCart() {
+    shoppingCart = [];
+    updateCartUI();
+}
+
+function checkoutCart() {
+    if (shoppingCart.length === 0) return;
+    const total = cartTotalEl.textContent;
+    alert(`Zahlung erfolgreich abgeschlossen!\nBetrag: ${total}\nKassenzettel gedruckt.`);
+    clearCart();
+}
+
+function updateCartUI() {
+    cartList.innerHTML = "";
+    
+    if (shoppingCart.length === 0) {
+        emptyCartMsg.style.display = "flex";
+        cartSubtotalEl.textContent = "0,00 €";
+        cartTotalEl.textContent = "0,00 €";
+        return;
+    }
+    
+    emptyCartMsg.style.display = "none";
+    let subtotal = 0;
+    
+    shoppingCart.forEach(item => {
+        subtotal += item.total;
+        
+        const row = document.createElement("div");
+        row.className = "cart-item-row";
+        
+        const details = document.createElement("div");
+        details.className = "item-details";
+        
+        const name = document.createElement("span");
+        name.className = "item-name";
+        name.textContent = item.name;
+        
+        const meta = document.createElement("span");
+        meta.className = "item-meta";
+        if (item.unit === "kg") {
+            meta.textContent = `${item.quantity.toFixed(3)} kg x ${item.pricePerUnit.toFixed(2)} €/kg`;
+        } else {
+            meta.textContent = `${item.quantity} Stk. x ${item.pricePerUnit.toFixed(2)} €/Stk.`;
+        }
+        
+        details.appendChild(name);
+        details.appendChild(meta);
+        
+        const actions = document.createElement("div");
+        actions.className = "item-price-actions";
+        
+        const price = document.createElement("span");
+        price.className = "item-price";
+        price.textContent = `${item.total.toFixed(2).replace('.', ',')} €`;
+        
+        const delBtn = document.createElement("button");
+        delBtn.className = "delete-item-btn";
+        delBtn.onclick = () => deleteCartItem(item.timestamp);
+        delBtn.innerHTML = `
+            <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><polyline points="3 6 5 6 21 6"/><path d="M19 6v14a2 2 0 0 1-2 2H7a2 2 0 0 1-2-2V6m3 0V4a2 2 0 0 1 2-2h4a2 2 0 0 1 2 2v2"/></svg>
+        `;
+        
+        actions.appendChild(price);
+        actions.appendChild(delBtn);
+        
+        row.appendChild(details);
+        row.appendChild(actions);
+        
+        cartList.appendChild(row);
+    });
+    
+    const tax = subtotal * 0.07; // 7% vat
+    cartSubtotalEl.textContent = `${subtotal.toFixed(2).replace('.', ',')} €`;
+    cartTotalEl.textContent = `${subtotal.toFixed(2).replace('.', ',')} €`;
+}
+
+// --- DEVELOPER DASHBOARD BACKEND CALLS ---
+
+// 1. Synthetic dataset trigger
+async function generateDataset() {
+    try {
+        const response = await fetch("/api/generate_dataset", { method: "POST" });
+        const data = await response.json();
+        alert(data.message);
+        startTrainingPolling(); // will start checking generation status
+    } catch (err) {
+        console.error("Failed to generate dataset:", err);
+    }
+}
+
+// 2. Refresh Albumentations augmented preview
+async function refreshAugmentedPreview() {
+    const previewBox = document.getElementById("aug-preview-box");
+    previewBox.innerHTML = `<div class="preview-placeholder">Generiere Vorschau...</div>`;
+    
+    try {
+        const response = await fetch("/api/augmented_preview");
+        const data = await response.json();
+        if (data.status === "success") {
+            previewBox.innerHTML = `<img src="${data.image}" class="preview-image" alt="Original vs. Augmentiert">`;
+        } else {
+            previewBox.innerHTML = `<div class="preview-placeholder text-danger">Fehler: ${data.message}</div>`;
+        }
+    } catch (err) {
+        previewBox.innerHTML = `<div class="preview-placeholder text-danger">Keine Trainingsbilder vorhanden.<br>Bitte zuerst "Datensatz generieren" klicken.</div>`;
+    }
+}
+
+// 3. Trigger model training
+async function startTraining() {
+    const epochs = parseInt(document.getElementById("param-epochs").value) || 15;
+    const batchSize = parseInt(document.getElementById("param-batch").value) || 8;
+    
+    try {
+        const response = await fetch("/api/train", {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ epochs: epochs, batch_size: batchSize })
+        });
+        const data = await response.json();
+        alert(data.message);
+        startTrainingPolling();
+    } catch (err) {
+        console.error("Failed to start training:", err);
+    }
+}
+
+// Poll training API
+function startTrainingPolling() {
+    if (trainingPollInterval) return;
+    
+    const checkStatus = async () => {
+        try {
+            const response = await fetch("/api/train_status");
+            const data = await response.json();
+            
+            updateTrainingStatusUI(data);
+            
+            // If training or data generation completed or failed, we can stop/keep checking depending on state
+            if (data.status === "completed" || data.status === "failed" || data.status === "idle") {
+                // Keep checking periodically but slower, or stop if we want to
+            }
+        } catch (err) {
+            console.error("Status check failed:", err);
+        }
+    };
+    
+    checkStatus();
+    trainingPollInterval = setInterval(checkStatus, 1500);
+}
+
+function stopTrainingPolling() {
+    if (trainingPollInterval) {
+        clearInterval(trainingPollInterval);
+        trainingPollInterval = null;
+    }
+}
+
+// Update training UI and plot graphs
+function updateTrainingStatusUI(data) {
+    const trainingState = document.getElementById("training-state");
+    const trainingEpoch = document.getElementById("training-epoch");
+    const progressFill = document.getElementById("training-progress-fill");
+    const logMsg = document.getElementById("training-log-msg");
+    const statusBox = document.getElementById("training-status-box");
+    
+    // Update status labels
+    trainingState.textContent = getGermanState(data.status);
+    trainingEpoch.textContent = data.epoch > 0 ? `Epoche: ${data.epoch}/${data.total_epochs}` : "Epoche: -/-";
+    progressFill.style.width = `${data.progress}%`;
+    logMsg.textContent = data.message;
+    
+    // Update box glow styling
+    statusBox.className = "training-status-box";
+    if (data.status === "training") {
+        statusBox.classList.add("active-pulse");
+    }
+    
+    // Plot Chart if loss history is available
+    if (data.train_loss && data.train_loss.length > 0) {
+        updateChart(data.train_loss, data.val_loss || []);
+    }
+}
+
+function getGermanState(status) {
+    switch (status) {
+        case "idle": return "Bereit";
+        case "generating_data": return "Generiere Daten...";
+        case "training": return "Training läuft...";
+        case "completed": return "Abgeschlossen";
+        case "failed": return "Fehlgeschlagen";
+        default: return status;
+    }
+}
+
+// Plot losses in SVG
+function updateChart(trainLosses, valLosses) {
+    const svgWidth = 440; // width bounds inside SVG 500
+    const svgHeight = 150; // height bounds inside SVG 200
+    
+    if (trainLosses.length === 0) return;
+    
+    // Find maximum loss to scale Y-axis correctly
+    const allLosses = [...trainLosses, ...valLosses];
+    const maxLoss = Math.max(2.0, ...allLosses);
+    
+    const getSvgCoords = (losses) => {
+        return losses.map((loss, idx) => {
+            const x = 40 + (idx / (losses.length - 1 || 1)) * svgWidth;
+            const y = 170 - (loss / maxLoss) * svgHeight;
+            return `${x.toFixed(1)},${y.toFixed(1)}`;
+        });
+    };
+    
+    const trainPoints = getSvgCoords(trainLosses);
+    document.getElementById("train-loss-path").setAttribute("d", "M " + trainPoints.join(" L "));
+    
+    if (valLosses.length > 0) {
+        const valPoints = getSvgCoords(valLosses);
+        document.getElementById("val-loss-path").setAttribute("d", "M " + valPoints.join(" L "));
+    }
+}
+
+// 4. Export model to ONNX
+async function exportModel() {
+    const onnxLog = document.getElementById("onnx-log");
+    const exportBtn = document.getElementById("export-onnx-btn");
+    const latencyVal = document.getElementById("onnx-latency");
+    const latencyStatus = document.getElementById("onnx-latency-status");
+    
+    onnxLog.textContent = "Starte ONNX Export & Benchmarking...";
+    exportBtn.disabled = true;
+    
+    try {
+        const response = await fetch("/api/export", { method: "POST" });
+        const data = await response.json();
+        
+        exportBtn.disabled = false;
+        
+        if (data.status === "success") {
+            onnxLog.textContent = data.log || "Export erfolgreich!";
+            
+            // Extract latency from benchmark logs using regex
+            const match = data.log.match(/Average ONNX Runtime inference latency: ([\d\.]+) ms/);
+            if (match && match[1]) {
+                const latency = match[1];
+                latencyVal.textContent = `${latency} ms`;
+                latencyStatus.textContent = parseFloat(latency) < 50.0 
+                    ? "✓ < 50 ms (Flüssige Live-Ansicht)"
+                    : "⚠️ > 50 ms (Optimiere Hardware)";
+            } else {
+                latencyVal.textContent = "< 30 ms";
+                latencyStatus.textContent = "✓ Inferenz läuft flüssig";
+            }
+            alert("Modell erfolgreich in ONNX konvertiert und geladen!");
+        } else {
+            onnxLog.textContent = `Fehler: ${data.message}`;
+            alert(`Export fehlgeschlagen: ${data.message}`);
+        }
+    } catch (err) {
+        exportBtn.disabled = false;
+        onnxLog.textContent = `Fehler bei der Server-Kommunikation.`;
+        alert("Export-Anfrage fehlgeschlagen. Vergewissere dich, dass ein PyTorch Modell (.pt) existiert.");
+    }
+}
+
+async function captureForTraining() {
+    if (!isWebcamActive) {
+        alert("Bitte aktivieren Sie zuerst die Live-Webcam, um ein echtes Foto aufzunehmen.");
+        return;
+    }
+    
+    const classSelect = document.getElementById("capture-class-select");
+    const selectedClass = classSelect.value;
+    if (!selectedClass) return;
+    
+    const captureBtn = document.getElementById("capture-image-btn");
+    captureBtn.disabled = true;
+    
+    // Capture current frame from canvas (high quality)
+    const base64Frame = canvasEl.toDataURL("image/jpeg", 0.95);
+    
+    try {
+        const response = await fetch("/api/capture_training_image", {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ image: base64Frame, class_name: selectedClass })
+        });
+        const data = await response.json();
+        captureBtn.disabled = false;
+        
+        if (data.status === "success") {
+            // Update display count
+            document.getElementById("capture-count-display").textContent = `Eigene Bilder: ${data.count}`;
+            
+            // Flash camera box as visual feedback
+            const container = document.querySelector(".camera-card");
+            container.classList.add("scale-flash");
+            setTimeout(() => container.classList.remove("scale-flash"), 300);
+            
+            console.log(data.message);
+        } else {
+            alert(`Fehler bei Aufnahme: ${data.message}`);
+        }
+    } catch (err) {
+        captureBtn.disabled = false;
+        console.error("Capture request failed:", err);
+        alert("Serverfehler bei Bild-Erfassung.");
+    }
+}
+
+async function initCapturePanel() {
+    const classSelect = document.getElementById("capture-class-select");
+    if (!classSelect) return;
+    
+    classSelect.innerHTML = "";
+    Object.keys(PRICING_DATABASE).forEach(key => {
+        const opt = document.createElement("option");
+        opt.value = key;
+        opt.textContent = PRICING_DATABASE[key].name;
+        if (key === "kartoffel") {
+            opt.selected = true; // default to kartoffel
+        }
+        classSelect.appendChild(opt);
+    });
+    
+    try {
+        const response = await fetch("/api/capture_count");
+        const data = await response.json();
+        document.getElementById("capture-count-display").textContent = `Eigene Bilder: ${data.count}`;
+    } catch (err) {
+        console.error("Failed to load initial capture count:", err);
+    }
+}
+
+// --- VIRTUAL BARCODE SCANNER LOGIC ---
+
+// Initial load of EAN database on startup
+async function initArticles() {
+    try {
+        const response = await fetch("/api/articles");
+        const data = await response.json();
+        articlesDatabase = data;
+        
+        // Sync names back to PRICING_DATABASE
+        Object.keys(articlesDatabase).forEach(key => {
+            if (!PRICING_DATABASE[key]) {
+                PRICING_DATABASE[key] = { name: articlesDatabase[key].name, price: 1.99, unit: "Stk." };
+            } else {
+                PRICING_DATABASE[key].name = articlesDatabase[key].name;
+            }
+        });
+    } catch (err) {
+        console.error("Fehler beim Initialisieren der Artikeldatenbank:", err);
+    }
+}
+
+// Render articles table rows in Article Management tab
+function renderArticlesTable() {
+    const tbody = document.getElementById("articles-table-body");
+    if (!tbody) return;
+    tbody.innerHTML = "";
+    
+    Object.keys(articlesDatabase).forEach(key => {
+        const article = articlesDatabase[key];
+        const tr = document.createElement("tr");
+        tr.style.borderBottom = "1px solid var(--border-glass)";
+        
+        tr.innerHTML = `
+            <td style="padding: 12px 16px; font-family: monospace; color: var(--text-secondary);">${key}</td>
+            <td style="padding: 12px 16px;">
+                <input type="text" class="article-name-input input-field" data-key="${key}" value="${article.name}" style="width: 100%; max-width: 250px; background: rgba(255,255,255,0.05); border: 1px solid var(--border-glass); color: var(--text-primary); padding: 6px 12px; border-radius: 4px; font-size: 13px;">
+            </td>
+            <td style="padding: 12px 16px;">
+                <input type="text" class="article-ean-input input-field" data-key="${key}" value="${article.ean}" style="width: 100%; max-width: 250px; background: rgba(255,255,255,0.05); border: 1px solid var(--border-glass); color: var(--text-primary); padding: 6px 12px; border-radius: 4px; font-size: 13px;" placeholder="z.B. 4001234000018">
+            </td>
+        `;
+        tbody.appendChild(tr);
+    });
+}
+
+// Load articles table in Article Management tab
+async function loadArticlesDatabase() {
+    try {
+        const response = await fetch("/api/articles");
+        const data = await response.json();
+        articlesDatabase = data;
+        
+        // Sync to PRICING_DATABASE
+        Object.keys(articlesDatabase).forEach(key => {
+            if (!PRICING_DATABASE[key]) {
+                PRICING_DATABASE[key] = { name: articlesDatabase[key].name, price: 1.99, unit: "Stk." };
+            } else {
+                PRICING_DATABASE[key].name = articlesDatabase[key].name;
+            }
+        });
+        
+        renderArticlesTable();
+    } catch (err) {
+        console.error("Fehler beim Laden der Artikeldatenbank:", err);
+    }
+}
+
+// Prompt to add a new article key and EAN code
+function addNewArticlePrompt() {
+    const key = prompt("Geben Sie einen eindeutigen Systemschlüssel für den Artikel ein (z.B. 'mango', nur Kleinbuchstaben und Unterstriche):");
+    if (!key) return;
+    
+    // Validate key: lowercase, letters, numbers, underscores
+    const keyRegex = /^[a-z0-9_]+$/;
+    if (!keyRegex.test(key)) {
+        alert("Ungültiger Schlüssel. Bitte nur Kleinbuchstaben (a-z), Zahlen (0-9) und Unterstriche (_) verwenden.");
+        return;
+    }
+    
+    if (PRICING_DATABASE[key] || articlesDatabase[key]) {
+        alert("Dieser Artikel-Schlüssel existiert bereits.");
+        return;
+    }
+    
+    const name = prompt("Geben Sie den Anzeigenamen für den Artikel ein (z.B. 'Mango'):");
+    if (!name) return;
+    
+    const ean = prompt("Geben Sie den EAN-Barcode für die Tastaturemulation ein (z.B. '4001234000292'):");
+    if (!ean) return;
+    
+    // Add to local articlesDatabase and PRICING_DATABASE
+    articlesDatabase[key] = { name: name, ean: ean };
+    PRICING_DATABASE[key] = { name: name, price: 1.99, unit: "Stk." };
+    
+    // Reload the table and capture dropdown
+    renderArticlesTable();
+    initCapturePanel();
+}
+
+// Save articles database
+async function saveArticlesDatabase() {
+    const tbody = document.getElementById("articles-table-body");
+    if (!tbody) return;
+    
+    const nameInputs = tbody.querySelectorAll(".article-name-input");
+    const eanInputs = tbody.querySelectorAll(".article-ean-input");
+    
+    const updatedArticles = {};
+    nameInputs.forEach((input, index) => {
+        const key = input.getAttribute("data-key");
+        const name = input.value.trim();
+        const ean = eanInputs[index].value.trim();
+        updatedArticles[key] = { name, ean };
+    });
+    
+    try {
+        const response = await fetch("/api/articles", {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ articles: updatedArticles })
+        });
+        const data = await response.json();
+        if (data.status === "success") {
+            articlesDatabase = updatedArticles;
+            
+            // Sync updated articles to PRICING_DATABASE
+            Object.keys(updatedArticles).forEach(key => {
+                if (!PRICING_DATABASE[key]) {
+                    PRICING_DATABASE[key] = { name: updatedArticles[key].name, price: 1.99, unit: "Stk." };
+                } else {
+                    PRICING_DATABASE[key].name = updatedArticles[key].name;
+                }
+            });
+            
+            // Update capture dropdown
+            initCapturePanel();
+            
+            alert("Artikeldatenbank erfolgreich gespeichert!");
+        } else {
+            alert("Fehler beim Speichern: " + data.message);
+        }
+    } catch (err) {
+        console.error("Fehler beim Speichern der Artikeldatenbank:", err);
+        alert("Serverfehler beim Speichern der Artikeldatenbank.");
+    }
+}
+
+// Play high-pitched scanner beep
+function playScannerBeep() {
+    try {
+        const audioCtx = new (window.AudioContext || window.webkitAudioContext)();
+        const oscillator = audioCtx.createOscillator();
+        const gainNode = audioCtx.createGain();
+        
+        oscillator.connect(gainNode);
+        gainNode.connect(audioCtx.destination);
+        
+        oscillator.type = "sine";
+        oscillator.frequency.setValueAtTime(1000, audioCtx.currentTime); // 1kHz beep
+        
+        gainNode.gain.setValueAtTime(0, audioCtx.currentTime);
+        gainNode.gain.linearRampToValueAtTime(0.2, audioCtx.currentTime + 0.01);
+        gainNode.gain.exponentialRampToValueAtTime(0.01, audioCtx.currentTime + 0.15);
+        
+        oscillator.start(audioCtx.currentTime);
+        oscillator.stop(audioCtx.currentTime + 0.16);
+    } catch (err) {
+        console.error("Audio Context failed to play beep:", err);
+    }
+}
+
+// Add log message to the terminal container
+function addScanLog(msg) {
+    const logEl = document.getElementById("scanner-log");
+    if (!logEl) return;
+    
+    const timeStr = new Date().toLocaleTimeString([], { hour: '2-digit', minute: '2-digit', second: '2-digit' });
+    const logLine = `[${timeStr}] ${msg}`;
+    
+    if (logEl.textContent === "Warte auf Erkennung...") {
+        logEl.textContent = logLine;
+    } else {
+        logEl.textContent += "\\n" + logLine;
+    }
+    
+    logEl.scrollTop = logEl.scrollHeight;
+}
+
+// Send EAN code to backend scanner endpoint
+async function sendEanToPos(classKey) {
+    const article = articlesDatabase[classKey] || { name: PRICING_DATABASE[classKey]?.name || classKey, ean: "" };
+    
+    if (!article.ean) {
+        addScanLog(`⚠️ Fehler: Kein EAN-Code für ${article.name} definiert.`);
+        return;
+    }
+    
+    addScanLog(`Scanne ${article.name} (EAN: ${article.ean})...`);
+    
+    try {
+        const response = await fetch("/api/scan_ean", {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ class_name: classKey })
+        });
+        const data = await response.json();
+        
+        if (response.ok && data.status === "success") {
+            playScannerBeep();
+            addScanLog(`✓ EAN ${article.ean} erfolgreich gesendet.`);
+            
+            // Visual flash feedback on scanner card
+            const scannerCard = document.querySelector(".scanner-card");
+            if (scannerCard) {
+                scannerCard.classList.add("scale-flash");
+                setTimeout(() => scannerCard.classList.remove("scale-flash"), 400);
+            }
+        } else {
+            addScanLog(`❌ Fehler: ${data.message || "Tastaturemulation fehlgeschlagen"}`);
+        }
+    } catch (err) {
+        console.error("Scan API error:", err);
+        addScanLog(`❌ Serverfehler beim Senden des EAN-Codes.`);
+    }
+}
+
+// Stability filter logic
+async function processStabilityAndScan(predictions) {
+    const autoScanToggle = document.getElementById("auto-scan-toggle");
+    const autoScan = autoScanToggle ? autoScanToggle.checked : true;
+    
+    // Find the prediction with the highest confidence
+    let topPrediction = null;
+    if (predictions && predictions.length > 0) {
+        predictions.forEach(p => {
+            if (!topPrediction || p.confidence > topPrediction.confidence) {
+                topPrediction = p;
+            }
+        });
+    }
+    
+    const scannerIndicator = document.getElementById("scanner-indicator");
+    const progressWrapper = document.getElementById("stability-progress-wrapper");
+    const progressFill = document.getElementById("stability-progress-fill");
+    
+    const STABLE_CONF_THRESHOLD = 0.75;
+    
+    if (topPrediction && topPrediction.confidence >= STABLE_CONF_THRESHOLD) {
+        const detectedClass = topPrediction.class;
+        
+        if (stableClass !== detectedClass) {
+            stableClass = detectedClass;
+            stableCount = 1;
+        } else {
+            stableCount++;
+        }
+        
+        if (progressWrapper && progressFill) {
+            progressWrapper.style.display = "block";
+            const percent = Math.min(100, (stableCount / STABILITY_THRESHOLD) * 100);
+            progressFill.style.width = `${percent}%`;
+        }
+        
+        if (scannerIndicator) {
+            const articleName = PRICING_DATABASE[detectedClass]?.name || detectedClass;
+            scannerIndicator.textContent = `Erkenne ${articleName}... (${stableCount}/${STABILITY_THRESHOLD})`;
+            scannerIndicator.style.color = "var(--color-accent)";
+        }
+        
+        if (stableCount >= STABILITY_THRESHOLD) {
+            if (lastScannedClass !== detectedClass && !scanLockActive) {
+                if (autoScan) {
+                    await sendEanToPos(detectedClass);
+                } else {
+                    addScanLog(`[Auto-Scan aus] ${PRICING_DATABASE[detectedClass]?.name || detectedClass} stabil erkannt.`);
+                }
+                lastScannedClass = detectedClass;
+                scanLockActive = true;
+            }
+            
+            if (progressFill) progressFill.style.width = "0%";
+            if (progressWrapper) progressWrapper.style.display = "none";
+            
+            if (scannerIndicator) {
+                scannerIndicator.textContent = `Gesperrt (Waage leeren)`;
+                scannerIndicator.style.color = "var(--color-danger)";
+            }
+        }
+    } else {
+        const CLEAR_THRESHOLD = 0.15;
+        
+        if (!topPrediction || topPrediction.confidence < CLEAR_THRESHOLD) {
+            stableClass = null;
+            stableCount = 0;
+            scanLockActive = false;
+            lastScannedClass = null;
+            
+            if (progressWrapper) progressWrapper.style.display = "none";
+            if (progressFill) progressFill.style.width = "0%";
+            if (scannerIndicator) {
+                scannerIndicator.textContent = "Bereit (Warte auf Objekt)";
+                scannerIndicator.style.color = "var(--color-success)";
+            }
+        } else {
+            stableCount = Math.max(0, stableCount - 1);
+            if (progressWrapper && progressFill) {
+                const percent = (stableCount / STABILITY_THRESHOLD) * 100;
+                progressFill.style.width = `${percent}%`;
+            }
+            if (scannerIndicator && stableCount > 0) {
+                scannerIndicator.textContent = `Signal instabil...`;
+                scannerIndicator.style.color = "var(--color-warning)";
+            } else if (scannerIndicator && !scanLockActive) {
+                scannerIndicator.textContent = "Bereit (Signal instabil)";
+                scannerIndicator.style.color = "var(--color-success)";
+            }
+        }
+    }
+}
+
+// Manual scan trigger
+async function triggerManualScan() {
+    if (stableClass) {
+        addScanLog(`Manueller Scan ausgelöst für ${PRICING_DATABASE[stableClass]?.name || stableClass}...`);
+        await sendEanToPos(stableClass);
+    } else {
+        addScanLog(`⚠️ Kein stabiles Objekt auf der Waage. Manueller Scan abgebrochen.`);
+        alert("Bitte legen Sie ein Produkt auf die Waage, um es zu scannen.");
+    }
+}
+
+// --- INTERACTIVE EVENT LISTENERS ---
+toggleCameraBtn.addEventListener("click", () => {
+    if (isWebcamActive) {
+        stopWebcam();
+    } else {
+        startWebcam();
+    }
+});
+
+triggerScaleBtn.addEventListener("click", () => {
+    // Flash scale overlay on manual trigger
+    const overlay = document.querySelector(".camera-card");
+    overlay.classList.add("scale-flash");
+    setTimeout(() => overlay.classList.remove("scale-flash"), 400);
+    
+    // Explicitly run detection
+    runDetection();
+});
+
+autoDetectToggle.addEventListener("change", (e) => {
+    autoDetectEnabled = e.target.checked;
+});
+
+// App Startup Initialization
+window.addEventListener("DOMContentLoaded", () => {
+    // Start in simulated video mode by default
+    startSimulatedStream();
+    
+    // Fetch articles from database
+    initArticles();
+    
+    // Read parameters from local values
+    updateCartUI();
+    
+    // Initialize capture classes
+    initCapturePanel();
+});
diff --git a/app/static/index.html b/app/static/index.html
new file mode 100644
index 0000000..dfe440e
--- /dev/null
+++ b/app/static/index.html
@@ -0,0 +1,382 @@
+<!DOCTYPE html>
+<html lang="de">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>FruitVision AI - POS Objekterkennung</title>
+    <link rel="preconnect" href="https://fonts.googleapis.com">
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Outfit:wght@400;500;600;700&display=swap" rel="stylesheet">
+    <link rel="stylesheet" href="styles.css">
+</head>
+<body>
+    <div class="app-container">
+        <!-- Header -->
+        <header class="app-header">
+            <div class="header-logo">
+                <svg width="32" height="32" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+                    <path d="M12 2C6.48 2 2 6.48 2 12C2 17.52 6.48 22 12 22C17.52 22 22 17.52 22 12C22 6.48 17.52 2 12 2ZM12 20C7.59 20 4 16.41 4 12C4 7.59 7.59 4 12 4C16.41 4 20 7.59 20 12C20 16.41 16.41 20 12 20Z" fill="var(--color-primary)"/>
+                    <path d="M11 6H13V12H11V6ZM11 14H13V16H11V14Z" fill="var(--color-primary)"/>
+                    <circle cx="12" cy="12" r="3" fill="var(--color-accent)"/>
+                </svg>
+                <h1>FruitVision <span class="badge">POS-AI</span></h1>
+            </div>
+            
+            <nav class="header-nav">
+                <button class="nav-btn active" id="tab-btn-checkout" onclick="switchTab('checkout')">
+                    <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><rect x="2" y="4" width="20" height="16" rx="2"/><line x1="12" y1="4" x2="12" y2="20"/><line x1="2" y1="12" x2="22" y2="12"/></svg>
+                    Kassensystem
+                </button>
+                <button class="nav-btn" id="tab-btn-articles" onclick="switchTab('articles')">
+                    <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M14 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V8z"/><polyline points="14 2 14 8 20 8"/><line x1="16" y1="13" x2="8" y2="13"/><line x1="16" y1="17" x2="8" y2="17"/><circle cx="10" cy="9" r="1"/></svg>
+                    Artikel-Verwaltung
+                </button>
+                <button class="nav-btn" id="tab-btn-dashboard" onclick="switchTab('dashboard')">
+                    <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><line x1="18" y1="20" x2="18" y2="10"/><line x1="12" y1="20" x2="12" y2="4"/><line x1="6" y1="20" x2="6" y2="14"/></svg>
+                    Developer-Dashboard
+                </button>
+            </nav>
+        </header>
+
+        <!-- Main Content Grid -->
+        <main class="app-main">
+            
+            <!-- CHECKOUT TAB -->
+            <section id="tab-checkout" class="tab-content active">
+                <div class="checkout-grid">
+                    <!-- Left: Camera Frame & Scale -->
+                    <div class="card camera-card">
+                        <div class="card-header">
+                            <div>
+                                <h2 class="card-title">Kamera-Vorschau</h2>
+                                <p class="card-subtitle">Positioniere das lose Obst & Gemüse auf der Waage</p>
+                            </div>
+                            <span class="status-indicator live" id="stream-status">Simuliert</span>
+                        </div>
+                        
+                        <div class="camera-viewport-container">
+                            <video id="webcam" autoplay playsinline muted style="display: none;"></video>
+                            <canvas id="camera-canvas" width="640" height="480"></canvas>
+                            
+                            <!-- Calibration Overlay / Grid -->
+                            <div class="scale-target-overlay">
+                                <div class="scale-target-box"></div>
+                            </div>
+                            
+                            <div class="stream-badge" id="camera-type-badge">SIMULATION-MODUS</div>
+                        </div>
+                        
+                        <div class="camera-controls">
+                            <button id="toggle-camera-btn" class="btn btn-secondary">
+                                <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><path d="M23 19a2 2 0 0 1-2 2H3a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h4l2-3h6l2 3h4a2 2 0 0 1 2 2z"/><circle cx="12" cy="13" r="4"/></svg>
+                                Webcam aktivieren
+                            </button>
+                            <button id="trigger-scale-btn" class="btn btn-primary btn-glow">
+                                <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><rect x="6" y="2" width="12" height="20" rx="2"/><path d="M12 18h.01"/></svg>
+                                Waage auslösen (Trigger)
+                            </button>
+                            <label class="toggle-control">
+                                <input type="checkbox" id="auto-detect-toggle" checked>
+                                <span class="toggle-slider"></span>
+                                <span class="toggle-label">Auto-Detect</span>
+                            </label>
+                        </div>
+
+                        <!-- Real Data Capture Panel -->
+                        <div class="capture-training-panel">
+                            <h3>Reales Produktbild erfassen (Datensammler)</h3>
+                            <div class="capture-controls-row">
+                                <select id="capture-class-select">
+                                    <!-- Options populated by JS -->
+                                </select>
+                                <button id="capture-image-btn" class="btn btn-accent" onclick="captureForTraining()">
+                                    <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><path d="M23 19a2 2 0 0 1-2 2H3a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h4l2-3h6l2 3h4a2 2 0 0 1 2 2z"/><circle cx="12" cy="13" r="4"/></svg>
+                                    Bild aufnehmen
+                                </button>
+                                <span class="capture-count-badge" id="capture-count-display">Eigene Bilder: 0</span>
+                            </div>
+                            <p class="panel-info-text">
+                                Legen Sie Ihre echte Kartoffel mittig in das Zielkreuz und nehmen Sie 10-15 Bilder aus verschiedenen Winkeln/Entfernungen auf.
+                            </p>
+                        </div>
+                    </div>
+
+                    <!-- Right: Quick Selection & Shopping Cart -->
+                    <div class="checkout-sidebar">
+                        
+                        <!-- AI Recommendations / Schnellwahl -->
+                        <div class="card quick-select-card">
+                            <div class="card-header">
+                                <h2 class="card-title">KI Schnellwahltasten</h2>
+                                <p class="card-subtitle">Vorschläge über Confidence-Schwellenwert &gt; 10%</p>
+                            </div>
+                            <div class="quick-buttons-container" id="quick-selection-buttons">
+                                <div class="empty-state">
+                                    <p>Warte auf Objekterkennung...</p>
+                                    <span>Legen Sie Obst auf die Waage</span>
+                                </div>
+                            </div>
+                        </div>
+
+                        <!-- Virtual Barcode-Scanner (Tastatur-Emulator) -->
+                        <div class="card scanner-card">
+                            <div class="card-header">
+                                <h2 class="card-title">Virtueller Barcode-Scanner</h2>
+                                <label class="toggle-control">
+                                    <input type="checkbox" id="auto-scan-toggle" checked>
+                                    <span class="toggle-slider"></span>
+                                    <span class="toggle-label">Auto-Scan</span>
+                                </label>
+                            </div>
+                            <div class="card-body" style="padding: 16px 20px;">
+                                <div class="scanner-status-box" style="display: flex; justify-content: space-between; font-size: 13px; margin-bottom: 10px;">
+                                    <span class="scanner-status-label" style="color: var(--text-secondary);">Scanner-Status:</span>
+                                    <span class="scanner-indicator" id="scanner-indicator" style="font-weight: 600; color: var(--color-success);">Bereit</span>
+                                </div>
+                                <div class="progress-bar-container" id="stability-progress-wrapper" style="height: 6px; margin-bottom: 12px; display: none;">
+                                    <div class="progress-bar-fill" id="stability-progress-fill" style="width: 0%; background: var(--color-accent); box-shadow: 0 0 10px var(--color-accent-glow);"></div>
+                                </div>
+                                <div class="terminal-container" style="margin-top: 0; margin-bottom: 10px;">
+                                    <div class="terminal-header">Scanner-Verlauf (Keystroke Logs)</div>
+                                    <div class="scanner-log-output" id="scanner-log" style="font-family: monospace; font-size: 11px; color: var(--color-success); height: 80px; overflow-y: auto; line-height: 1.4;">Warte auf Erkennung...</div>
+                                </div>
+                                <button id="manual-scan-btn" class="btn btn-accent btn-glow" style="width: 100%;" onclick="triggerManualScan()">
+                                    <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><path d="M4 7V4h3M4 17v3h3m10-16h3v3m-3 13v3h3M8 12h8m-4-4v8"/></svg>
+                                    EAN an aktive Kasse senden
+                                </button>
+                            </div>
+                        </div>
+
+                        <!-- Shopping Cart / Bon -->
+                        <div class="card cart-card">
+                            <div class="card-header">
+                                <h2 class="card-title">Kassenzettel</h2>
+                                <p class="card-subtitle">Aktuelle Transaktion</p>
+                            </div>
+                            <div class="cart-items" id="cart-list">
+                                <div class="empty-cart-state" id="empty-cart-msg">
+                                    <svg width="48" height="48" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"><circle cx="9" cy="21" r="1"/><circle cx="20" cy="21" r="1"/><path d="M1 1h4l2.68 13.39a2 2 0 0 0 2 1.61h9.72a2 2 0 0 0 2-1.61L23 6H6"/></svg>
+                                    <p>Einkaufswagen ist leer</p>
+                                </div>
+                            </div>
+                            <div class="cart-summary">
+                                <div class="summary-row">
+                                    <span>Zwischensumme:</span>
+                                    <span id="cart-subtotal">0,00 €</span>
+                                </div>
+                                <div class="summary-row total">
+                                    <span>Gesamtsumme:</span>
+                                    <span id="cart-total">0,00 €</span>
+                                </div>
+                            </div>
+                            <div class="cart-actions">
+                                <button id="cancel-cart-btn" class="btn btn-danger" onclick="clearCart()">Storno</button>
+                                <button id="pay-btn" class="btn btn-success btn-glow" onclick="checkoutCart()">Bezahlen</button>
+                            </div>
+                        </div>
+                        
+                    </div>
+                </div>
+            </section>
+
+            <!-- DEVELOPER DASHBOARD TAB -->
+            <section id="tab-dashboard" class="tab-content">
+                <div class="dashboard-grid">
+                    
+                    <!-- Left: Pipeline Steps -->
+                    <div class="dashboard-column">
+                        
+                        <!-- Step 1: Synthetic Dataset & Augmentation -->
+                        <div class="card">
+                            <div class="card-header">
+                                <span class="step-num">Schritt 1</span>
+                                <h2 class="card-title">Synthetische Datenpipeline</h2>
+                                <p class="card-subtitle">Generiere Trainingsdaten & Augmentierungen (Albumentations)</p>
+                            </div>
+                            <div class="card-body">
+                                <div class="action-row">
+                                    <button class="btn btn-secondary" onclick="generateDataset()">
+                                        Datensatz generieren
+                                    </button>
+                                    <span class="action-desc">Erstellt 200 Trainings- & 50 Validierungsbilder (YOLO-Format)</span>
+                                </div>
+                                
+                                <div class="augmentation-section">
+                                    <div class="section-title-row">
+                                        <h3>Augmentierungs-Vorschau (Live-Pipeline)</h3>
+                                        <button class="btn btn-text" onclick="refreshAugmentedPreview()">Aktualisieren</button>
+                                    </div>
+                                    <p class="section-desc">Echtzeit-Störung (Shift, Rotate, Scale, Brightness, Noise) zur Vermeidung von Overfitting an Lichtbedingungen.</p>
+                                    
+                                    <div class="preview-container" id="aug-preview-box">
+                                        <div class="preview-placeholder">
+                                            Klicke auf "Aktualisieren" um ein Bild und seine Albumentations-Störung anzuzeigen.
+                                        </div>
+                                    </div>
+                                </div>
+                            </div>
+                        </div>
+
+                        <!-- Step 3: Export & Hardware-Performance -->
+                        <div class="card">
+                            <div class="card-header">
+                                <span class="step-num">Schritt 3</span>
+                                <h2 class="card-title">ONNX-Export & Hardware-Inferenz</h2>
+                                <p class="card-subtitle">Modell-Optimierung für flüssigen Kassenbetrieb unter 50 ms</p>
+                            </div>
+                            <div class="card-body">
+                                <div class="action-row">
+                                    <button class="btn btn-accent" id="export-onnx-btn" onclick="exportModel()">
+                                        Modell in ONNX exportieren
+                                    </button>
+                                    <span class="action-desc">Konvertiert PyTorch (.pt) in ONNX (.onnx) für die Inferenz-Engine.</span>
+                                </div>
+                                
+                                <div class="terminal-container">
+                                    <div class="terminal-header">ONNX Compiler Logs</div>
+                                    <pre class="terminal-output" id="onnx-log">Warte auf Export-Trigger...</pre>
+                                </div>
+                                
+                                <div class="metric-grid">
+                                    <div class="metric-card">
+                                        <span class="metric-label">Inferenz-Zeit (CPU/NPU)</span>
+                                        <span class="metric-value text-accent" id="onnx-latency">- ms</span>
+                                        <span class="metric-desc" id="onnx-latency-status">Warte auf Export</span>
+                                    </div>
+                                    <div class="metric-card">
+                                        <span class="metric-label">Lizenzen & Schutzrecht</span>
+                                        <span class="metric-value text-success">100% OK</span>
+                                        <span class="metric-desc">Royalty-Free (BSD / MIT), kein GPL-Copyleft</span>
+                                    </div>
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+
+                    <!-- Right: Model Training Dashboard -->
+                    <div class="dashboard-column">
+                        
+                        <!-- Step 2: Model Training -->
+                        <div class="card training-card">
+                            <div class="card-header">
+                                <span class="step-num">Schritt 2</span>
+                                <h2 class="card-title">Transfer Learning (PyTorch)</h2>
+                                <p class="card-subtitle">Modelltraining auf SSDLite MobileNetV3-Large Basis</p>
+                            </div>
+                            <div class="card-body">
+                                <div class="training-controls-row">
+                                    <div class="control-group">
+                                        <label for="param-epochs">Epochen</label>
+                                        <input type="number" id="param-epochs" value="15" min="5" max="100">
+                                    </div>
+                                    <div class="control-group">
+                                        <label for="param-batch">Batch Size</label>
+                                        <select id="param-batch">
+                                            <option value="4">4</option>
+                                            <option value="8" selected>8</option>
+                                            <option value="16">16</option>
+                                        </select>
+                                    </div>
+                                    <button class="btn btn-primary" id="start-training-btn" onclick="startTraining()">
+                                        Training starten
+                                    </button>
+                                </div>
+
+                                <!-- Training Status -->
+                                <div class="training-status-box" id="training-status-box">
+                                    <div class="status-header-row">
+                                        <span class="status-title">Status: <strong id="training-state">Bereit</strong></span>
+                                        <span class="status-epoch" id="training-epoch">Epoche: -/-</span>
+                                    </div>
+                                    
+                                    <div class="progress-bar-container">
+                                        <div class="progress-bar-fill" id="training-progress-fill" style="width: 0%"></div>
+                                    </div>
+                                    
+                                    <p class="status-log-message" id="training-log-msg">Klicke "Training starten" um den Feintuning-Prozess anzustoßen.</p>
+                                </div>
+
+                                <!-- Chart Area -->
+                                <div class="chart-container">
+                                    <h3>Lernkurve (Loss History)</h3>
+                                    <div class="chart-wrapper">
+                                        <svg viewBox="0 0 500 200" class="training-chart" id="training-loss-chart">
+                                            <!-- Grid Lines -->
+                                            <line x1="40" y1="20" x2="480" y2="20" stroke="#333842" stroke-dasharray="4"/>
+                                            <line x1="40" y1="70" x2="480" y2="70" stroke="#333842" stroke-dasharray="4"/>
+                                            <line x1="40" y1="120" x2="480" y2="120" stroke="#333842" stroke-dasharray="4"/>
+                                            <line x1="40" y1="170" x2="480" y2="170" stroke="#4a505e"/>
+                                            
+                                            <!-- Y-Axis Ticks -->
+                                            <text x="30" y="25" fill="#8a92a3" font-size="10" text-anchor="end">2.0</text>
+                                            <text x="30" y="75" fill="#8a92a3" font-size="10" text-anchor="end">1.0</text>
+                                            <text x="30" y="125" fill="#8a92a3" font-size="10" text-anchor="end">0.5</text>
+                                            <text x="30" y="175" fill="#8a92a3" font-size="10" text-anchor="end">0.0</text>
+                                            
+                                            <!-- Chart Curves -->
+                                            <path id="train-loss-path" d="" fill="none" stroke="var(--color-primary)" stroke-width="3"/>
+                                            <path id="val-loss-path" d="" fill="none" stroke="var(--color-accent)" stroke-width="2" stroke-dasharray="2"/>
+                                        </svg>
+                                    </div>
+                                    <div class="chart-legend">
+                                        <div class="legend-item"><span class="legend-color train"></span>Train Loss</div>
+                                        <div class="legend-item"><span class="legend-color val"></span>Validation Loss</div>
+                                    </div>
+                                </div>
+                            </div>
+                        </div>
+
+                    </div>
+                </div>
+            </section>
+
+            <!-- ARTICLE MANAGEMENT TAB -->
+            <section id="tab-articles" class="tab-content">
+                <div class="card">
+                    <div class="card-header">
+                        <div>
+                            <h2 class="card-title">Artikel- & EAN-Verwaltung</h2>
+                            <p class="card-subtitle">Hinterlegen Sie die Barcodes (EAN) Ihrer Produkte für den virtuellen Scanner</p>
+                        </div>
+                        <div style="display: flex; gap: 12px; align-items: center;">
+                            <button class="btn btn-secondary" onclick="addNewArticlePrompt()">
+                                + Artikel hinzufügen
+                            </button>
+                            <button class="btn btn-primary btn-glow" onclick="saveArticlesDatabase()">
+                                Änderungen speichern
+                            </button>
+                        </div>
+                    </div>
+                    <div class="card-body">
+                        <p style="font-size: 13px; color: var(--text-secondary); margin-bottom: 20px; line-height: 1.5;">
+                            Geben Sie für jede Produktklasse den gewünschten Barcode (EAN) ein. Wenn das System dieses Produkt stabil erkennt, simuliert der <strong>virtuelle Tastatur-Scanner</strong> diesen Barcode direkt in Ihr Kassenfenster (gefolgt von Enter).
+                        </p>
+                        
+                        <div class="articles-table-wrapper" style="overflow-x: auto;">
+                            <table class="articles-table" style="width: 100%; border-collapse: collapse; text-align: left;">
+                                <thead>
+                                    <tr style="border-bottom: 1px solid var(--border-glass); color: var(--text-secondary); font-size: 11px; text-transform: uppercase; letter-spacing: 0.5px;">
+                                        <th style="padding: 12px 16px;">Klasse (ML-Key)</th>
+                                        <th style="padding: 12px 16px;">Name (Anzeige)</th>
+                                        <th style="padding: 12px 16px;">EAN-Barcode (Tastaturemulation)</th>
+                                    </tr>
+                                </thead>
+                                <tbody id="articles-table-body" style="font-size: 14px;">
+                                    <!-- Dynamic rows loaded by JS -->
+                                </tbody>
+                            </table>
+                        </div>
+                    </div>
+                </div>
+            </section>
+        </main>
+        
+        <!-- Footer -->
+        <footer class="app-footer">
+            <p>Lizenz-Fokus: 100% kommerziell nutzbar, BSD & MIT Open-Source Lizenzen. ONNX Runtime Inference.</p>
+        </footer>
+    </div>
+    
+    <script src="app.js"></script>
+</body>
+</html>
diff --git a/app/static/styles.css b/app/static/styles.css
new file mode 100644
index 0000000..2e1d87a
--- /dev/null
+++ b/app/static/styles.css
@@ -0,0 +1,1075 @@
+/* Global CSS Variables & Theme Setup */
+:root {
+    --color-bg-darkest: #0a0d14;
+    --color-bg-dark: #101420;
+    --color-bg-card: rgba(21, 27, 43, 0.75);
+    --color-bg-input: #151b2a;
+    --color-primary: #6366f1; /* Vibrant Indigo */
+    --color-primary-glow: rgba(99, 102, 241, 0.35);
+    --color-accent: #a855f7; /* Electric Purple */
+    --color-accent-glow: rgba(168, 85, 247, 0.25);
+    --color-success: #10b981; /* Emerald Green */
+    --color-success-glow: rgba(16, 185, 129, 0.25);
+    --color-warning: #f59e0b; /* Amber */
+    --color-danger: #ef4444; /* Rose Red */
+    
+    --text-primary: #f3f4f6;
+    --text-secondary: #9ca3af;
+    --text-muted: #6b7280;
+    
+    --border-glass: rgba(255, 255, 255, 0.08);
+    --border-focus: rgba(99, 102, 241, 0.5);
+    --shadow-soft: 0 10px 30px -10px rgba(0, 0, 0, 0.5);
+    --shadow-glow: 0 0 20px var(--color-primary-glow);
+    
+    --font-heading: 'Outfit', sans-serif;
+    --font-body: 'Inter', sans-serif;
+    
+    --border-radius-lg: 16px;
+    --border-radius-md: 10px;
+    --border-radius-sm: 6px;
+}
+
+/* Reset & Base Styles */
+* {
+    box-sizing: border-box;
+    margin: 0;
+    padding: 0;
+}
+
+body {
+    background-color: var(--color-bg-darkest);
+    background-image: radial-gradient(circle at 10% 20%, rgba(99, 102, 241, 0.05) 0%, transparent 40%),
+                      radial-gradient(circle at 90% 80%, rgba(168, 85, 247, 0.05) 0%, transparent 40%);
+    background-attachment: fixed;
+    color: var(--text-primary);
+    font-family: var(--font-body);
+    -webkit-font-smoothing: antialiased;
+    min-height: 100vh;
+}
+
+.app-container {
+    display: flex;
+    flex-direction: column;
+    min-height: 100vh;
+    padding: 0 24px;
+    max-width: 1600px;
+    margin: 0 auto;
+}
+
+/* Header styling */
+.app-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    padding: 20px 0;
+    border-bottom: 1px solid var(--border-glass);
+}
+
+.header-logo {
+    display: flex;
+    align-items: center;
+    gap: 12px;
+}
+
+.header-logo h1 {
+    font-family: var(--font-heading);
+    font-size: 24px;
+    font-weight: 700;
+    letter-spacing: -0.5px;
+    color: var(--text-primary);
+}
+
+.header-logo h1 span.badge {
+    font-size: 11px;
+    font-family: var(--font-body);
+    font-weight: 600;
+    letter-spacing: 0.5px;
+    padding: 3px 8px;
+    background: linear-gradient(135deg, var(--color-primary), var(--color-accent));
+    border-radius: 20px;
+    margin-left: 6px;
+    vertical-align: middle;
+    box-shadow: 0 0 10px rgba(99, 102, 241, 0.2);
+}
+
+.header-nav {
+    display: flex;
+    gap: 12px;
+    background: rgba(16, 20, 32, 0.6);
+    padding: 4px;
+    border-radius: var(--border-radius-md);
+    border: 1px solid var(--border-glass);
+    backdrop-filter: blur(8px);
+}
+
+.nav-btn {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    background: transparent;
+    border: none;
+    color: var(--text-secondary);
+    padding: 8px 16px;
+    font-family: var(--font-body);
+    font-size: 14px;
+    font-weight: 500;
+    border-radius: var(--border-radius-sm);
+    cursor: pointer;
+    transition: all 0.2s ease;
+}
+
+.nav-btn:hover {
+    color: var(--text-primary);
+    background: rgba(255, 255, 255, 0.04);
+}
+
+.nav-btn.active {
+    color: var(--text-primary);
+    background: var(--color-primary);
+    box-shadow: 0 4px 12px rgba(99, 102, 241, 0.25);
+}
+
+/* Main layouts */
+.app-main {
+    flex-grow: 1;
+    padding: 24px 0;
+}
+
+.tab-content {
+    display: none;
+    animation: fadeIn 0.4s cubic-bezier(0.4, 0, 0.2, 1) forwards;
+}
+
+.tab-content.active {
+    display: block;
+}
+
+@keyframes fadeIn {
+    from { opacity: 0; transform: translateY(8px); }
+    to { opacity: 1; transform: translateY(0); }
+}
+
+/* Card components */
+.card {
+    background: var(--color-bg-card);
+    border: 1px solid var(--border-glass);
+    border-radius: var(--border-radius-lg);
+    box-shadow: var(--shadow-soft);
+    backdrop-filter: blur(16px);
+    overflow: hidden;
+    margin-bottom: 24px;
+}
+
+.card-header {
+    padding: 20px 24px;
+    border-bottom: 1px solid var(--border-glass);
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+}
+
+.card-title {
+    font-family: var(--font-heading);
+    font-size: 18px;
+    font-weight: 600;
+    color: var(--text-primary);
+}
+
+.card-subtitle {
+    font-size: 13px;
+    color: var(--text-secondary);
+    margin-top: 2px;
+}
+
+.card-body {
+    padding: 24px;
+}
+
+/* Checkout View Layout */
+.checkout-grid {
+    display: grid;
+    grid-template-columns: 1.6fr 1fr;
+    gap: 24px;
+    align-items: start;
+}
+
+@media (max-width: 1024px) {
+    .checkout-grid {
+        grid-template-columns: 1fr;
+    }
+}
+
+.camera-card {
+    margin-bottom: 0;
+}
+
+.camera-viewport-container {
+    position: relative;
+    width: 100%;
+    aspect-ratio: 4/3;
+    max-height: 520px;
+    background: #000;
+    border-bottom: 1px solid var(--border-glass);
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    overflow: hidden;
+}
+
+#camera-canvas {
+    width: 100%;
+    height: 100%;
+    object-fit: cover;
+}
+
+/* Weight Scale target frame overlap overlay */
+.scale-target-overlay {
+    position: absolute;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    pointer-events: none;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+.scale-target-box {
+    width: 70%;
+    height: 65%;
+    border: 2px dashed rgba(255, 255, 255, 0.25);
+    border-radius: var(--border-radius-lg);
+    box-shadow: 0 0 0 9999px rgba(0, 0, 0, 0.4);
+    position: relative;
+    transition: all 0.3s ease;
+}
+
+.scale-target-box::before, .scale-target-box::after {
+    content: '';
+    position: absolute;
+    width: 24px;
+    height: 24px;
+    border-color: rgba(255, 255, 255, 0.5);
+    border-style: solid;
+}
+
+.scale-target-box::before {
+    top: -2px; left: -2px; border-width: 3px 0 0 3px; border-top-left-radius: 8px;
+}
+.scale-target-box::after {
+    bottom: -2px; right: -2px; border-width: 0 3px 3px 0; border-bottom-right-radius: 8px;
+}
+
+.scale-flash .scale-target-box {
+    border-color: var(--color-success);
+    box-shadow: 0 0 0 9999px rgba(0, 0, 0, 0.2), 0 0 40px var(--color-success-glow);
+}
+
+.stream-badge {
+    position: absolute;
+    top: 16px;
+    left: 16px;
+    background: rgba(0, 0, 0, 0.7);
+    border: 1px solid var(--border-glass);
+    padding: 4px 10px;
+    border-radius: var(--border-radius-sm);
+    font-size: 11px;
+    font-weight: 600;
+    letter-spacing: 0.5px;
+    color: var(--text-primary);
+    backdrop-filter: blur(4px);
+}
+
+.status-indicator {
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    font-size: 12px;
+    font-weight: 500;
+    padding: 4px 10px;
+    background: rgba(255, 255, 255, 0.05);
+    border: 1px solid var(--border-glass);
+    border-radius: 20px;
+}
+
+.status-indicator::before {
+    content: '';
+    display: inline-block;
+    width: 8px;
+    height: 8px;
+    border-radius: 50%;
+    background: var(--text-muted);
+}
+
+.status-indicator.live::before {
+    background: var(--color-success);
+    box-shadow: 0 0 8px var(--color-success);
+    animation: pulse 1.5s infinite;
+}
+
+.status-indicator.training::before {
+    background: var(--color-warning);
+    box-shadow: 0 0 8px var(--color-warning);
+    animation: pulse 1.5s infinite;
+}
+
+@keyframes pulse {
+    0% { transform: scale(0.9); opacity: 0.6; }
+    50% { transform: scale(1.2); opacity: 1; }
+    100% { transform: scale(0.9); opacity: 0.6; }
+}
+
+.camera-controls {
+    padding: 16px 24px;
+    display: flex;
+    align-items: center;
+    gap: 16px;
+}
+
+/* POS Quick buttons layout */
+.quick-buttons-container {
+    padding: 20px;
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(130px, 1fr));
+    gap: 12px;
+    min-height: 100px;
+}
+
+.quick-btn {
+    background: linear-gradient(135deg, rgba(255, 255, 255, 0.04), rgba(255, 255, 255, 0.01));
+    border: 1px solid var(--border-glass);
+    color: var(--text-primary);
+    padding: 14px;
+    border-radius: var(--border-radius-md);
+    cursor: pointer;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    gap: 6px;
+    transition: all 0.2s cubic-bezier(0.4, 0, 0.2, 1);
+}
+
+.quick-btn:hover {
+    transform: translateY(-2px);
+    border-color: var(--color-primary);
+    background: rgba(99, 102, 241, 0.08);
+}
+
+.quick-btn:active {
+    transform: translateY(0);
+}
+
+.quick-btn .btn-label {
+    font-size: 13px;
+    font-weight: 500;
+    text-align: center;
+    text-transform: capitalize;
+}
+
+.quick-btn .btn-conf {
+    font-size: 11px;
+    font-weight: 600;
+    padding: 2px 6px;
+    border-radius: 20px;
+}
+
+/* Style conf badge color dynamically based on class values */
+.quick-btn.high-conf {
+    border-color: rgba(16, 185, 129, 0.4);
+    background: rgba(16, 185, 129, 0.05);
+}
+.quick-btn.high-conf:hover {
+    background: rgba(16, 185, 129, 0.1);
+    border-color: var(--color-success);
+}
+.quick-btn.high-conf .btn-conf {
+    background: var(--color-success-glow);
+    color: #34d399;
+}
+
+.quick-btn.low-conf .btn-conf {
+    background: rgba(255, 255, 255, 0.05);
+    color: var(--text-secondary);
+}
+
+.empty-state {
+    grid-column: 1 / -1;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    justify-content: center;
+    color: var(--text-muted);
+    padding: 24px;
+    text-align: center;
+}
+
+.empty-state span {
+    font-size: 12px;
+    margin-top: 4px;
+}
+
+/* Shopping Cart styling - Kassenzettel */
+.cart-card {
+    display: none !important;
+}
+
+.cart-items {
+    padding: 20px 24px;
+    flex-grow: 1;
+    min-height: 180px;
+    max-height: 320px;
+    overflow-y: auto;
+    border-bottom: 1px dashed var(--border-glass);
+}
+
+.empty-cart-state {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    justify-content: center;
+    color: var(--text-muted);
+    height: 100%;
+    gap: 8px;
+    padding: 24px;
+}
+
+.empty-cart-state p {
+    font-size: 14px;
+}
+
+.cart-item-row {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    padding: 10px 0;
+    border-bottom: 1px solid rgba(255, 255, 255, 0.03);
+}
+
+.cart-item-row:last-child {
+    border-bottom: none;
+}
+
+.item-details {
+    display: flex;
+    flex-direction: column;
+}
+
+.item-name {
+    font-weight: 500;
+    font-size: 14px;
+    text-transform: capitalize;
+}
+
+.item-meta {
+    font-size: 11px;
+    color: var(--text-secondary);
+    margin-top: 2px;
+}
+
+.item-price-actions {
+    display: flex;
+    align-items: center;
+    gap: 16px;
+}
+
+.item-price {
+    font-weight: 600;
+    font-size: 14px;
+}
+
+.delete-item-btn {
+    background: transparent;
+    border: none;
+    color: var(--text-muted);
+    cursor: pointer;
+    padding: 4px;
+    border-radius: 4px;
+    transition: all 0.2s ease;
+}
+
+.delete-item-btn:hover {
+    color: var(--color-danger);
+    background: rgba(239, 68, 68, 0.08);
+}
+
+.cart-summary {
+    padding: 16px 24px;
+    background: rgba(0, 0, 0, 0.15);
+    border-bottom: 1px solid var(--border-glass);
+}
+
+.summary-row {
+    display: flex;
+    justify-content: space-between;
+    font-size: 13px;
+    color: var(--text-secondary);
+    margin-bottom: 6px;
+}
+
+.summary-row:last-child {
+    margin-bottom: 0;
+}
+
+.summary-row.total {
+    font-family: var(--font-heading);
+    font-size: 18px;
+    font-weight: 700;
+    color: var(--text-primary);
+    margin-top: 8px;
+    padding-top: 8px;
+    border-top: 1px solid var(--border-glass);
+}
+
+.cart-actions {
+    padding: 16px 24px;
+    display: flex;
+    gap: 12px;
+}
+
+.cart-actions button {
+    flex-grow: 1;
+}
+
+/* Button UI system */
+.btn {
+    display: inline-flex;
+    align-items: center;
+    justify-content: center;
+    gap: 8px;
+    padding: 10px 20px;
+    font-family: var(--font-body);
+    font-size: 14px;
+    font-weight: 500;
+    border-radius: var(--border-radius-md);
+    border: 1px solid transparent;
+    cursor: pointer;
+    transition: all 0.2s cubic-bezier(0.4, 0, 0.2, 1);
+}
+
+.btn:active {
+    transform: scale(0.98);
+}
+
+.btn-primary {
+    background: var(--color-primary);
+    color: #fff;
+    box-shadow: 0 4px 14px rgba(99, 102, 241, 0.3);
+}
+.btn-primary:hover {
+    background: #4f46e5;
+    transform: translateY(-1px);
+}
+
+.btn-secondary {
+    background: rgba(255, 255, 255, 0.04);
+    border-color: var(--border-glass);
+    color: var(--text-primary);
+}
+.btn-secondary:hover {
+    background: rgba(255, 255, 255, 0.08);
+    transform: translateY(-1px);
+}
+
+.btn-accent {
+    background: var(--color-accent);
+    color: #fff;
+    box-shadow: 0 4px 14px rgba(168, 85, 247, 0.3);
+}
+.btn-accent:hover {
+    background: #9333ea;
+    transform: translateY(-1px);
+}
+
+.btn-success {
+    background: var(--color-success);
+    color: #fff;
+    box-shadow: 0 4px 14px rgba(16, 185, 129, 0.3);
+}
+.btn-success:hover {
+    background: #059669;
+    transform: translateY(-1px);
+}
+
+.btn-danger {
+    background: rgba(239, 68, 68, 0.1);
+    border-color: rgba(239, 68, 68, 0.2);
+    color: var(--color-danger);
+}
+.btn-danger:hover {
+    background: var(--color-danger);
+    color: #fff;
+    transform: translateY(-1px);
+}
+
+.btn-text {
+    background: transparent;
+    border: none;
+    color: var(--color-primary);
+    padding: 4px 8px;
+    border-radius: 4px;
+    font-size: 13px;
+}
+.btn-text:hover {
+    background: rgba(99, 102, 241, 0.08);
+}
+
+.btn-glow:hover {
+    box-shadow: 0 0 25px var(--color-primary-glow);
+}
+.btn-success.btn-glow:hover {
+    box-shadow: 0 0 25px var(--color-success-glow);
+}
+
+/* Toggle Switches */
+.toggle-control {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    cursor: pointer;
+    user-select: none;
+}
+
+.toggle-slider {
+    position: relative;
+    display: inline-block;
+    width: 44px;
+    height: 24px;
+    background-color: rgba(255, 255, 255, 0.1);
+    border: 1px solid var(--border-glass);
+    border-radius: 20px;
+    transition: all 0.3s ease;
+}
+
+.toggle-slider::before {
+    content: '';
+    position: absolute;
+    height: 16px;
+    width: 16px;
+    left: 3px;
+    bottom: 3px;
+    background-color: var(--text-secondary);
+    border-radius: 50%;
+    transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+}
+
+input:checked + .toggle-slider {
+    background-color: var(--color-primary);
+    border-color: var(--color-primary);
+}
+
+input:checked + .toggle-slider::before {
+    transform: translateX(20px);
+    background-color: #fff;
+}
+
+.toggle-label {
+    font-size: 13px;
+    font-weight: 500;
+    color: var(--text-primary);
+}
+
+.toggle-control input {
+    display: none;
+}
+
+/* Developer Dashboard layout */
+.dashboard-grid {
+    display: grid;
+    grid-template-columns: 1fr 1.1fr;
+    gap: 24px;
+}
+
+@media (max-width: 1024px) {
+    .dashboard-grid {
+        grid-template-columns: 1fr;
+    }
+}
+
+.dashboard-column {
+    display: flex;
+    flex-direction: column;
+}
+
+.step-num {
+    font-size: 10px;
+    font-weight: 700;
+    letter-spacing: 1px;
+    text-transform: uppercase;
+    color: var(--color-accent);
+    padding: 3px 8px;
+    background: var(--color-accent-glow);
+    border-radius: 4px;
+    border: 1px solid rgba(168, 85, 247, 0.15);
+}
+
+.action-row {
+    display: flex;
+    align-items: center;
+    gap: 20px;
+    margin-bottom: 20px;
+}
+
+.action-desc {
+    font-size: 13px;
+    color: var(--text-secondary);
+    line-height: 1.4;
+}
+
+/* Preview Augmentations styling */
+.augmentation-section {
+    border-top: 1px solid var(--border-glass);
+    padding-top: 20px;
+    margin-top: 20px;
+}
+
+.section-title-row {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: 6px;
+}
+
+.section-title-row h3 {
+    font-size: 15px;
+    font-weight: 600;
+}
+
+.section-desc {
+    font-size: 12px;
+    color: var(--text-muted);
+    margin-bottom: 12px;
+    line-height: 1.4;
+}
+
+.preview-container {
+    width: 100%;
+    aspect-ratio: 2/1;
+    background: #090b10;
+    border: 1px solid var(--border-glass);
+    border-radius: var(--border-radius-md);
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    overflow: hidden;
+}
+
+.preview-placeholder {
+    color: var(--text-muted);
+    font-size: 12px;
+    text-align: center;
+    padding: 24px;
+    max-width: 80%;
+}
+
+.preview-image {
+    width: 100%;
+    height: 100%;
+    object-fit: contain;
+}
+
+/* Terminal compiler output simulation */
+.terminal-container {
+    background: #090c12;
+    border: 1px solid var(--border-glass);
+    border-radius: var(--border-radius-md);
+    padding: 12px;
+    margin-top: 12px;
+    margin-bottom: 20px;
+}
+
+.terminal-header {
+    font-family: monospace;
+    font-size: 11px;
+    color: var(--text-muted);
+    margin-bottom: 6px;
+    padding-bottom: 4px;
+    border-bottom: 1px solid rgba(255, 255, 255, 0.03);
+}
+
+.terminal-output {
+    font-family: 'Courier New', Courier, monospace;
+    font-size: 11px;
+    color: #38bdf8; /* light blue */
+    white-space: pre-wrap;
+    max-height: 120px;
+    overflow-y: auto;
+    line-height: 1.4;
+}
+
+/* Metrics Dashboard Cards */
+.metric-grid {
+    display: grid;
+    grid-template-columns: 1fr 1fr;
+    gap: 16px;
+}
+
+.metric-card {
+    background: rgba(0, 0, 0, 0.15);
+    border: 1px solid var(--border-glass);
+    border-radius: var(--border-radius-md);
+    padding: 16px;
+    display: flex;
+    flex-direction: column;
+}
+
+.metric-label {
+    font-size: 11px;
+    font-weight: 500;
+    color: var(--text-secondary);
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+}
+
+.metric-value {
+    font-family: var(--font-heading);
+    font-size: 26px;
+    font-weight: 700;
+    margin: 8px 0 4px 0;
+}
+
+.metric-desc {
+    font-size: 11px;
+    color: var(--text-muted);
+}
+
+.text-accent {
+    color: var(--color-accent);
+}
+.text-success {
+    color: var(--color-success);
+}
+
+/* Training panel inputs & controls */
+.training-controls-row {
+    display: flex;
+    align-items: flex-end;
+    gap: 16px;
+    margin-bottom: 24px;
+}
+
+.control-group {
+    display: flex;
+    flex-direction: column;
+    gap: 6px;
+}
+
+.control-group label {
+    font-size: 12px;
+    font-weight: 500;
+    color: var(--text-secondary);
+}
+
+.control-group input, .control-group select {
+    background: var(--color-bg-input);
+    border: 1px solid var(--border-glass);
+    border-radius: var(--border-radius-md);
+    padding: 8px 12px;
+    color: var(--text-primary);
+    font-family: var(--font-body);
+    font-size: 14px;
+    width: 100px;
+}
+
+.control-group input:focus, .control-group select:focus {
+    outline: none;
+    border-color: var(--color-primary);
+}
+
+.training-controls-row button {
+    flex-grow: 1;
+}
+
+/* Training status monitor card */
+.training-status-box {
+    background: rgba(0, 0, 0, 0.2);
+    border: 1px solid var(--border-glass);
+    border-radius: var(--border-radius-md);
+    padding: 16px;
+    margin-bottom: 24px;
+}
+
+.status-header-row {
+    display: flex;
+    justify-content: space-between;
+    font-size: 13px;
+    margin-bottom: 10px;
+}
+
+.status-epoch {
+    color: var(--text-secondary);
+}
+
+.progress-bar-container {
+    width: 100%;
+    height: 8px;
+    background: rgba(255, 255, 255, 0.05);
+    border-radius: 10px;
+    overflow: hidden;
+    margin-bottom: 12px;
+}
+
+.progress-bar-fill {
+    height: 100%;
+    background: linear-gradient(90deg, var(--color-primary), var(--color-accent));
+    border-radius: 10px;
+    transition: width 0.4s ease;
+    box-shadow: 0 0 10px var(--color-primary-glow);
+}
+
+.status-log-message {
+    font-size: 12px;
+    color: var(--text-secondary);
+    line-height: 1.4;
+}
+
+/* Training chart */
+.chart-container h3 {
+    font-size: 14px;
+    font-weight: 600;
+    color: var(--text-primary);
+    margin-bottom: 12px;
+}
+
+.chart-wrapper {
+    background: #090c12;
+    border: 1px solid var(--border-glass);
+    border-radius: var(--border-radius-md);
+    padding: 12px;
+}
+
+.training-chart {
+    width: 100%;
+    height: auto;
+    overflow: visible;
+}
+
+.training-chart text {
+    font-family: monospace;
+}
+
+.chart-legend {
+    display: flex;
+    gap: 16px;
+    justify-content: center;
+    margin-top: 10px;
+}
+
+.legend-item {
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    font-size: 11px;
+    color: var(--text-secondary);
+}
+
+.legend-color {
+    width: 12px;
+    height: 4px;
+    border-radius: 2px;
+    display: inline-block;
+}
+
+.legend-color.train {
+    background: var(--color-primary);
+}
+
+.legend-color.val {
+    background: var(--color-accent);
+    border-top: 1px dashed #fff; /* indicates dash array */
+}
+
+/* Real Data Capture Panel Styles */
+.capture-training-panel {
+    padding: 16px 24px;
+    border-top: 1px dashed var(--border-glass);
+    background: rgba(0, 0, 0, 0.12);
+}
+
+.capture-training-panel h3 {
+    font-size: 13px;
+    font-weight: 600;
+    margin-bottom: 10px;
+    color: var(--text-primary);
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+}
+
+.capture-controls-row {
+    display: flex;
+    gap: 12px;
+    align-items: center;
+    margin-bottom: 8px;
+}
+
+.capture-controls-row select {
+    flex-grow: 1;
+    background: var(--color-bg-input);
+    border: 1px solid var(--border-glass);
+    border-radius: var(--border-radius-md);
+    padding: 8px 12px;
+    color: var(--text-primary);
+    font-family: var(--font-body);
+    font-size: 14px;
+    outline: none;
+}
+
+.capture-controls-row select:focus {
+    border-color: var(--color-accent);
+}
+
+.capture-count-badge {
+    font-size: 12px;
+    font-weight: 600;
+    padding: 6px 12px;
+    background: var(--color-accent-glow);
+    border: 1px solid rgba(168, 85, 247, 0.2);
+    color: var(--color-accent);
+    border-radius: var(--border-radius-sm);
+    white-space: nowrap;
+}
+
+.panel-info-text {
+    font-size: 11px;
+    color: var(--text-muted);
+    line-height: 1.4;
+}
+
+/* Article Management Table Styles */
+.articles-table th, .articles-table td {
+    padding: 12px 16px;
+    border-bottom: 1px solid var(--border-glass);
+}
+
+.articles-table tbody tr:hover {
+    background: rgba(255, 255, 255, 0.02);
+}
+
+.articles-table input {
+    background: var(--color-bg-input);
+    border: 1px solid var(--border-glass);
+    border-radius: var(--border-radius-sm);
+    padding: 6px 12px;
+    color: var(--text-primary);
+    font-family: monospace;
+    font-size: 14px;
+    width: 100%;
+    max-width: 250px;
+    outline: none;
+    transition: all 0.2s ease;
+}
+
+.articles-table input:focus {
+    border-color: var(--color-primary);
+    box-shadow: 0 0 8px var(--color-primary-glow);
+}
+
+/* Footer styling */
+.app-footer {
+    border-top: 1px solid var(--border-glass);
+    padding: 16px 0;
+    text-align: center;
+    color: var(--text-muted);
+    font-size: 11px;
+}
diff --git a/articles.json b/articles.json
new file mode 100644
index 0000000..7669785
--- /dev/null
+++ b/articles.json
@@ -0,0 +1,114 @@
+{
+  "apfel_elstar": {
+    "name": "Apfel Elstar",
+    "ean": "4001234000018"
+  },
+  "apfel_gala": {
+    "name": "Apfel Gala",
+    "ean": "4001234000025"
+  },
+  "apfel_granny_smith": {
+    "name": "Apfel Granny Smith",
+    "ean": "4001234000032"
+  },
+  "banane_chiquita": {
+    "name": "Banane Chiquita",
+    "ean": "4001234000049"
+  },
+  "banane_bio": {
+    "name": "Bio Banane",
+    "ean": "4001234000056"
+  },
+  "birne": {
+    "name": "Birne Abate Fetel",
+    "ean": "4001234000063"
+  },
+  "orange": {
+    "name": "Orangen",
+    "ean": "4001234000070"
+  },
+  "zitrone": {
+    "name": "Zitronen",
+    "ean": "4001234000087"
+  },
+  "limette": {
+    "name": "Limetten",
+    "ean": "4001234000094"
+  },
+  "erdbeere": {
+    "name": "Erdbeeren Schale",
+    "ean": "4001234000100"
+  },
+  "blaubeere": {
+    "name": "Kulturheidelbeeren",
+    "ean": "4001234000117"
+  },
+  "weintraube_hell": {
+    "name": "Tafeltrauben hell",
+    "ean": "4001234000124"
+  },
+  "weintraube_dunkel": {
+    "name": "Tafeltrauben dunkel",
+    "ean": "4001234000131"
+  },
+  "pfirsich": {
+    "name": "Pfirsiche",
+    "ean": "4001234000148"
+  },
+  "tomate": {
+    "name": "Rispentomaten",
+    "ean": "4001234000155"
+  },
+  "gurke": {
+    "name": "Salatgurke",
+    "ean": "4001234000162"
+  },
+  "kartoffel": {
+    "name": "Speisekartoffeln",
+    "ean": "4001234000163"
+  },
+  "karotte": {
+    "name": "Speisem\u00c3\u00b6hren",
+    "ean": "4001234000186"
+  },
+  "zwiebel_gelb": {
+    "name": "Speisezwiebeln",
+    "ean": "4001234000193"
+  },
+  "zwiebel_rot": {
+    "name": "Rote Zwiebeln",
+    "ean": "4001234000209"
+  },
+  "knoblauch": {
+    "name": "Knoblauch",
+    "ean": "4001234000216"
+  },
+  "brokkoli": {
+    "name": "Brokkoli",
+    "ean": "4001234000223"
+  },
+  "paprika_rot": {
+    "name": "Paprika rot",
+    "ean": "4001234000230"
+  },
+  "paprika_gelb": {
+    "name": "Paprika gelb",
+    "ean": "4001234000247"
+  },
+  "paprika_gruen": {
+    "name": "Paprika gr\u00c3\u00bcn",
+    "ean": "4001234000254"
+  },
+  "champignon": {
+    "name": "Champignons wei\u00c3\u0178",
+    "ean": "4001234000261"
+  },
+  "zucchini": {
+    "name": "Zucchini",
+    "ean": "4001234000278"
+  },
+  "avocado": {
+    "name": "Avocado Hass",
+    "ean": "4001234000285"
+  }
+}
\ No newline at end of file
diff --git a/data_generator.py b/data_generator.py
new file mode 100644
index 0000000..f021afd
--- /dev/null
+++ b/data_generator.py
@@ -0,0 +1,475 @@
+import os
+import random
+import numpy as np
+from PIL import Image, ImageDraw, ImageFilter
+
+import json
+
+# Define expanded classes (28 classes)
+DEFAULT_CLASSES = [
+    "apfel_elstar",
+    "apfel_gala",
+    "apfel_granny_smith",
+    "banane_chiquita",
+    "banane_bio",
+    "birne",
+    "orange",
+    "zitrone",
+    "limette",
+    "erdbeere",
+    "blaubeere",
+    "weintraube_hell",
+    "weintraube_dunkel",
+    "pfirsich",
+    "tomate",
+    "gurke",
+    "kartoffel",
+    "karotte",
+    "zwiebel_gelb",
+    "zwiebel_rot",
+    "knoblauch",
+    "brokkoli",
+    "paprika_rot",
+    "paprika_gelb",
+    "paprika_gruen",
+    "champignon",
+    "zucchini",
+    "avocado"
+]
+
+def get_classes_from_db():
+    base_dir = os.path.dirname(os.path.abspath(__file__))
+    articles_path = os.path.join(base_dir, "articles.json")
+    if os.path.exists(articles_path):
+        try:
+            with open(articles_path, "r") as f:
+                articles = json.load(f)
+                keys = list(articles.keys())
+                if keys:
+                    return keys
+        except Exception:
+            pass
+    return DEFAULT_CLASSES.copy()
+
+CLASSES = get_classes_from_db()
+
+def reload_classes():
+    new_classes = get_classes_from_db()
+    CLASSES.clear()
+    CLASSES.extend(new_classes)
+    print(f"Reloaded CLASSES: {len(CLASSES)} classes in memory.")
+
+def draw_apple_elstar(draw):
+    draw.ellipse([45, 50, 155, 160], fill=(210, 45, 45, 255))
+    draw.ellipse([80, 45, 120, 60], fill=(210, 45, 45, 255))
+    draw.ellipse([55, 70, 110, 140], fill=(230, 140, 40, 180))
+    draw.line([(100, 55), (105, 25)], fill=(101, 67, 33, 255), width=4)
+    draw.ellipse([93, 50, 107, 57], fill=(60, 20, 20, 255))
+
+def draw_apple_gala(draw):
+    draw.ellipse([45, 50, 155, 160], fill=(240, 190, 60, 255))
+    draw.ellipse([80, 45, 120, 60], fill=(240, 190, 60, 255))
+    for offset in range(-35, 45, 15):
+        draw.arc([45 + offset, 50, 155 + offset, 160], start=30, end=150, fill=(210, 40, 40, 200), width=4)
+        draw.arc([45 - offset, 50, 155 - offset, 160], start=30, end=150, fill=(210, 40, 40, 200), width=4)
+    draw.line([(100, 55), (103, 25)], fill=(101, 67, 33, 255), width=4)
+    draw.ellipse([93, 50, 107, 57], fill=(70, 50, 20, 255))
+
+def draw_apple_granny_smith(draw):
+    # Bright green apple
+    draw.ellipse([45, 50, 155, 160], fill=(120, 200, 40, 255))
+    draw.ellipse([80, 45, 120, 60], fill=(120, 200, 40, 255))
+    # Light yellow highlight
+    draw.ellipse([60, 70, 95, 110], fill=(160, 230, 80, 180))
+    # Stem
+    draw.line([(100, 55), (105, 25)], fill=(101, 67, 33, 255), width=4)
+    draw.ellipse([93, 50, 107, 57], fill=(40, 70, 15, 255))
+
+def draw_banana_chiquita(draw):
+    banana_pts = [
+        (40, 130), (50, 100), (70, 75), (100, 60), (130, 60),
+        (160, 75), (170, 95), (160, 95), (135, 80), (110, 80),
+        (85, 90), (65, 110), (50, 140)
+    ]
+    draw.polygon(banana_pts, fill=(245, 215, 50, 255))
+    draw.polygon([(40, 130), (50, 140), (45, 145), (35, 135)], fill=(75, 100, 30, 255))
+    draw.polygon([(160, 75), (170, 95), (175, 90), (165, 70)], fill=(90, 75, 30, 255))
+    draw.ellipse([100, 65, 112, 75], fill=(10, 50, 150, 255))
+    draw.ellipse([103, 68, 109, 72], fill=(245, 215, 50, 255))
+
+def draw_banana_bio(draw):
+    banana_pts = [
+        (45, 125), (55, 100), (75, 80), (100, 70), (125, 70),
+        (150, 82), (160, 98), (152, 98), (130, 88), (110, 85),
+        (88, 93), (70, 110), (53, 133)
+    ]
+    draw.polygon(banana_pts, fill=(225, 220, 60, 255))
+    draw.polygon([(45, 125), (53, 133), (48, 138), (40, 130)], fill=(50, 90, 20, 255))
+    draw.polygon([(150, 82), (160, 98), (163, 93), (153, 77)], fill=(70, 90, 20, 255))
+    spots = [(70, 105), (85, 92), (100, 83), (115, 83), (130, 86)]
+    for sx, sy in spots:
+        draw.ellipse([sx-2, sy-2, sx+2, sy+2], fill=(80, 50, 20, 200))
+    draw.polygon([(95, 70), (105, 70), (102, 87), (92, 86)], fill=(120, 140, 90, 255))
+
+def draw_pear(draw):
+    # Birne: pear shape (narrow top, wide bottom)
+    draw.ellipse([65, 50, 135, 110], fill=(180, 190, 50, 255)) # top
+    draw.ellipse([50, 90, 150, 170], fill=(180, 190, 50, 255)) # bottom
+    draw.ellipse([60, 75, 140, 145], fill=(180, 190, 50, 255)) # mid fill
+    # Orange-red blush
+    draw.ellipse([95, 100, 140, 150], fill=(210, 110, 30, 120))
+    # Stem
+    draw.line([(100, 55), (105, 25)], fill=(101, 67, 33, 255), width=3)
+
+def draw_orange(draw):
+    draw.ellipse([45, 45, 155, 155], fill=(245, 120, 20, 255))
+    # Peel texture (little dots)
+    for _ in range(15):
+        tx = random.randint(60, 140)
+        ty = random.randint(60, 140)
+        draw.ellipse([tx, ty, tx+2, ty+2], fill=(215, 95, 5, 200))
+
+def draw_lemon(draw):
+    # Zitrone: yellow oval with pointed ends
+    draw.ellipse([50, 65, 150, 135], fill=(245, 225, 40, 255))
+    draw.polygon([(40, 100), (52, 90), (52, 110)], fill=(245, 225, 40, 255)) # left tip
+    draw.polygon([(160, 100), (148, 90), (148, 110)], fill=(245, 225, 40, 255)) # right tip
+
+def draw_lime(draw):
+    # Limette: green oval with pointed ends
+    draw.ellipse([55, 70, 145, 130], fill=(60, 160, 30, 255))
+    draw.polygon([(47, 100), (57, 92), (57, 108)], fill=(60, 160, 30, 255))
+    draw.polygon([(153, 100), (143, 92), (143, 108)], fill=(60, 160, 30, 255))
+
+def draw_strawberry(draw):
+    # Erdbeere: heart shape
+    draw.polygon([(100, 165), (55, 80), (100, 60), (145, 80)], fill=(225, 30, 50, 255))
+    draw.ellipse([55, 70, 105, 100], fill=(225, 30, 50, 255))
+    draw.ellipse([95, 70, 145, 100], fill=(225, 30, 50, 255))
+    # Seeds (yellow dots)
+    for _ in range(20):
+        sx = random.randint(65, 135)
+        sy = random.randint(75, 140)
+        draw.ellipse([sx, sy, sx+2, sy+2], fill=(240, 220, 100, 255))
+    # Leafy top
+    draw.polygon([(100, 65), (85, 45), (95, 60), (100, 40), (105, 60), (115, 45)], fill=(40, 160, 50, 255))
+
+def draw_blueberry(draw):
+    # Blaubeere: small blue circle
+    draw.ellipse([70, 70, 130, 130], fill=(45, 80, 160, 255))
+    # Crown (dark crown tip)
+    draw.polygon([(90, 70), (100, 62), (110, 70), (105, 75), (95, 75)], fill=(20, 45, 95, 255))
+
+def draw_grape_green(draw):
+    # Weintraube hell: cluster of small green circles
+    coords = [
+        (100, 60), (85, 75), (115, 75), (70, 95), (100, 95), (130, 95),
+        (85, 115), (115, 115), (100, 135), (100, 155)
+    ]
+    # Stem
+    draw.line([(100, 60), (100, 35)], fill=(110, 140, 50, 255), width=3)
+    for cx, cy in coords:
+        draw.ellipse([cx-16, cy-16, cx+16, cy+16], fill=(160, 210, 80, 255))
+        draw.ellipse([cx-16, cy-16, cx+16, cy+16], outline=(130, 180, 60, 255), width=1)
+
+def draw_grape_dark(draw):
+    # Weintraube dunkel: cluster of small dark purple circles
+    coords = [
+        (100, 60), (85, 75), (115, 75), (70, 95), (100, 95), (130, 95),
+        (85, 115), (115, 115), (100, 135), (100, 155)
+    ]
+    draw.line([(100, 60), (100, 35)], fill=(90, 70, 110, 255), width=3)
+    for cx, cy in coords:
+        draw.ellipse([cx-16, cy-16, cx+16, cy+16], fill=(65, 30, 105, 255))
+        draw.ellipse([cx-16, cy-16, cx+16, cy+16], outline=(45, 15, 80, 255), width=1)
+
+def draw_peach(draw):
+    # Pfirsich: fuzzy orange-pink circle
+    draw.ellipse([45, 50, 155, 160], fill=(245, 130, 80, 255))
+    draw.ellipse([47, 50, 120, 160], fill=(245, 80, 110, 255)) # pink cheek
+    # Indentation line
+    draw.arc([40, 50, 150, 160], start=270, end=90, fill=(185, 45, 45, 255), width=2)
+    # stem
+    draw.line([(100, 53), (102, 30)], fill=(90, 70, 40, 255), width=3)
+
+def draw_tomato(draw):
+    draw.ellipse([50, 55, 150, 155], fill=(225, 30, 30, 255))
+    draw.ellipse([65, 70, 85, 85], fill=(255, 255, 255, 180))
+    cx, cy = 100, 55
+    stem_pts = [
+        (cx, cy), (cx-15, cy-10), (cx-5, cy-2),
+        (cx, cy-20), (cx+5, cy-2), (cx+15, cy-10),
+        (cx+8, cy+5), (cx, cy+8), (cx-8, cy+5)
+    ]
+    draw.polygon(stem_pts, fill=(34, 139, 34, 255))
+    draw.line([(cx, cy), (cx-5, cy-25)], fill=(34, 139, 34, 255), width=4)
+
+def draw_cucumber(draw):
+    draw.rounded_rectangle([35, 80, 165, 120], radius=20, fill=(20, 100, 40, 255))
+    for x in range(50, 150, 15):
+        draw.line([(x, 85), (x+5, 115)], fill=(30, 130, 55, 255), width=2)
+    draw.ellipse([32, 95, 38, 105], fill=(139, 115, 85, 255))
+    draw.ellipse([162, 95, 168, 105], fill=(100, 80, 40, 255))
+
+def draw_potato(draw):
+    # Kartoffel: irregular brown-yellow oval
+    draw.rounded_rectangle([45, 65, 155, 135], radius=35, fill=(190, 160, 95, 255))
+    # Add potato eyes (dark brown dots)
+    eyes = [(65, 80), (135, 90), (115, 120), (80, 125), (100, 85)]
+    for ex, ey in eyes:
+        draw.ellipse([ex-2, ey-1, ex+2, ey+1], fill=(110, 85, 45, 255))
+        draw.arc([ex-3, ey-3, ex+3, ey+3], start=0, end=180, fill=(110, 85, 45, 255), width=1)
+
+def draw_carrot(draw):
+    # Karotte: tapered orange shape
+    draw.polygon([(40, 85), (160, 100), (40, 115)], fill=(245, 110, 20, 255))
+    draw.ellipse([35, 85, 45, 115], fill=(245, 110, 20, 255))
+    # Green leaves on one end (left)
+    draw.polygon([(38, 100), (15, 80), (30, 95), (10, 100), (30, 105), (15, 120)], fill=(45, 155, 55, 255))
+    # Texture lines
+    for x in range(60, 140, 20):
+        draw.line([(x, 92), (x, 108)], fill=(215, 85, 10, 255), width=2)
+
+def draw_onion_yellow(draw):
+    # Zwiebel gelb: golden bulb shape
+    draw.ellipse([50, 60, 150, 150], fill=(195, 145, 75, 255))
+    draw.polygon([(100, 40), (80, 65), (120, 65)], fill=(195, 145, 75, 255)) # top tip
+    # Root hairs at bottom
+    draw.line([(90, 150), (85, 162)], fill=(230, 210, 170, 255), width=2)
+    draw.line([(100, 150), (100, 165)], fill=(230, 210, 170, 255), width=2)
+    draw.line([(110, 150), (115, 162)], fill=(230, 210, 170, 255), width=2)
+
+def draw_onion_red(draw):
+    # Zwiebel rot: red-purple bulb shape
+    draw.ellipse([50, 60, 150, 150], fill=(140, 35, 95, 255))
+    draw.polygon([(100, 40), (80, 65), (120, 65)], fill=(140, 35, 95, 255))
+    # Light stripes
+    draw.arc([55, 60, 145, 150], start=180, end=360, fill=(185, 75, 135, 255), width=2)
+    # Root hairs
+    draw.line([(95, 150), (95, 162)], fill=(210, 190, 180, 255), width=2)
+    draw.line([(105, 150), (105, 162)], fill=(210, 190, 180, 255), width=2)
+
+def draw_garlic(draw):
+    # Knoblauch: off-white bulb with segment lines
+    draw.ellipse([50, 60, 150, 150], fill=(235, 230, 220, 255))
+    draw.polygon([(100, 42), (85, 65), (115, 65)], fill=(235, 230, 220, 255))
+    # Segment lines
+    draw.arc([65, 60, 135, 150], start=270, end=90, fill=(195, 190, 180, 255), width=2)
+    draw.arc([80, 60, 120, 150], start=270, end=90, fill=(195, 190, 180, 255), width=2)
+    # Roots
+    draw.line([(100, 150), (100, 160)], fill=(160, 150, 130, 255), width=2)
+
+def draw_broccoli(draw):
+    # Brokkoli: green tree-like structure
+    # Stalk
+    draw.rounded_rectangle([80, 110, 120, 170], radius=8, fill=(110, 175, 75, 255))
+    # Florets (overlapping circles)
+    draw.ellipse([60, 60, 110, 110], fill=(30, 110, 50, 255))
+    draw.ellipse([90, 50, 150, 100], fill=(30, 110, 50, 255))
+    draw.ellipse([75, 80, 135, 130], fill=(30, 110, 50, 255))
+    # Add tiny dots/texture
+    for _ in range(30):
+        tx = random.randint(65, 135)
+        ty = random.randint(55, 120)
+        draw.ellipse([tx, ty, tx+2, ty+2], fill=(60, 150, 80, 255))
+
+def draw_pepper_red(draw):
+    # Paprika rot: blocky rounded shape
+    draw.rounded_rectangle([50, 60, 150, 150], radius=25, fill=(210, 30, 30, 255))
+    # Lobes indentations
+    draw.arc([45, 60, 155, 150], start=230, end=310, fill=(160, 15, 15, 255), width=3)
+    # Green stem
+    draw.line([(100, 60), (100, 35)], fill=(45, 130, 45, 255), width=5)
+
+def draw_pepper_yellow(draw):
+    # Paprika gelb: blocky rounded shape
+    draw.rounded_rectangle([50, 60, 150, 150], radius=25, fill=(240, 190, 20, 255))
+    draw.arc([45, 60, 155, 150], start=230, end=310, fill=(195, 145, 10, 255), width=3)
+    draw.line([(100, 60), (100, 35)], fill=(45, 130, 45, 255), width=5)
+
+def draw_pepper_green(draw):
+    # Paprika grün: blocky rounded shape
+    draw.rounded_rectangle([50, 60, 150, 150], radius=25, fill=(35, 115, 45, 255))
+    draw.arc([45, 60, 155, 150], start=230, end=310, fill=(20, 85, 25, 255), width=3)
+    draw.line([(100, 60), (100, 35)], fill=(25, 80, 25, 255), width=5)
+
+def draw_mushroom(draw):
+    # Champignon: white-brown stalk and cap
+    # Stalk
+    draw.rounded_rectangle([85, 100, 115, 160], radius=6, fill=(225, 215, 205, 255))
+    # Cap (umbrella)
+    draw.ellipse([45, 60, 155, 120], fill=(210, 200, 190, 255))
+    draw.rectangle([45, 90, 155, 120], fill=(210, 200, 190, 255)) # bottom flat
+    # Gills (brown under cap)
+    draw.line([(45, 120), (155, 120)], fill=(120, 100, 90, 255), width=3)
+
+def draw_zucchini(draw):
+    # Zucchini: long cylinder, thicker than cucumber, dark green
+    draw.rounded_rectangle([30, 75, 170, 125], radius=22, fill=(15, 75, 35, 255))
+    # Speckled light green stripes
+    for x in range(45, 155, 20):
+        draw.line([(x, 80), (x+8, 120)], fill=(35, 115, 60, 180), width=3)
+    # Thick stem
+    draw.ellipse([167, 95, 173, 105], fill=(80, 95, 50, 255))
+
+def draw_avocado(draw):
+    # Halved avocado: pear outline, yellow-green center, brown seed
+    # Outer skin
+    draw.ellipse([60, 50, 140, 120], fill=(15, 45, 20, 255))
+    draw.ellipse([45, 85, 155, 170], fill=(15, 45, 20, 255))
+    # Light green inner meat
+    draw.ellipse([63, 53, 137, 117], fill=(185, 215, 110, 255))
+    draw.ellipse([49, 89, 151, 166], fill=(185, 215, 110, 255))
+    # Yellow center
+    draw.ellipse([58, 98, 142, 158], fill=(215, 235, 135, 255))
+    # Brown pit (seed) in center
+    draw.ellipse([78, 105, 122, 149], fill=(110, 65, 30, 255))
+    draw.ellipse([82, 109, 100, 125], fill=(255, 255, 255, 80)) # Pit reflection
+
+def create_object_image(class_idx, angle, scale):
+    obj_img = Image.new("RGBA", (200, 200), (0, 0, 0, 0))
+    draw = ImageDraw.Draw(obj_img)
+    
+    # Map class index to drawing functions
+    draw_funcs = [
+        draw_apple_elstar, draw_apple_gala, draw_apple_granny_smith,
+        draw_banana_chiquita, draw_banana_bio, draw_pear,
+        draw_orange, draw_lemon, draw_lime,
+        draw_strawberry, draw_blueberry, draw_grape_green,
+        draw_grape_dark, draw_peach, draw_tomato,
+        draw_cucumber, draw_potato, draw_carrot,
+        draw_onion_yellow, draw_onion_red, draw_garlic,
+        draw_broccoli, draw_pepper_red, draw_pepper_yellow,
+        draw_pepper_green, draw_mushroom, draw_zucchini,
+        draw_avocado
+    ]
+    
+    if class_idx < len(draw_funcs):
+        draw_funcs[class_idx](draw)
+        
+    rotated = obj_img.rotate(angle, resample=Image.Resampling.BILINEAR, expand=True)
+    
+    if scale != 1.0:
+        new_w = int(rotated.width * scale)
+        new_h = int(rotated.height * scale)
+        rotated = rotated.resize((new_w, new_h), resample=Image.Resampling.BILINEAR)
+        
+    bbox = rotated.getbbox()
+    if bbox is None:
+        return None, None
+        
+    cropped = rotated.crop(bbox)
+    return cropped, bbox
+
+def generate_background(width, height):
+    bg = Image.new("RGB", (width, height), (210, 215, 220))
+    draw = ImageDraw.Draw(bg)
+    grid_color = (185, 190, 195)
+    for x in range(0, width, 80):
+        draw.line([(x, 0), (x, height)], fill=grid_color, width=2)
+    for y in range(0, height, 80):
+        draw.line([(0, y), (width, y)], fill=grid_color, width=2)
+    draw.rectangle([10, 10, width-10, height-10], outline=(170, 175, 180), width=3)
+    
+    arr = np.array(bg, dtype=np.float32)
+    x_grad = np.linspace(-15, 15, width)
+    y_grad = np.linspace(-15, 15, height)
+    xx, yy = np.meshgrid(x_grad, y_grad)
+    arr[:, :, 0] += xx + yy
+    arr[:, :, 1] += xx + yy
+    arr[:, :, 2] += xx + yy
+    noise = np.random.normal(0, 3, (height, width, 3))
+    arr += noise
+    arr = np.clip(arr, 0, 255).astype(np.uint8)
+    return Image.fromarray(arr)
+
+def generate_sample(width=640, height=480):
+    bg = generate_background(width, height)
+    num_objects = random.randint(1, 3)
+    annotations = []
+    placed_boxes = []
+    
+    for _ in range(num_objects):
+        class_idx = random.randint(0, len(CLASSES)-1)
+        angle = random.randint(0, 360)
+        scale = random.uniform(0.7, 1.2)
+        
+        obj_img, _ = create_object_image(class_idx, angle, scale)
+        if obj_img is None:
+            continue
+            
+        ow, oh = obj_img.size
+        placed = False
+        
+        for attempt in range(25):
+            x = random.randint(30, width - ow - 30)
+            y = random.randint(30, height - oh - 30)
+            
+            overlap = False
+            for px1, py1, px2, py2 in placed_boxes:
+                ix1 = max(x, px1)
+                iy1 = max(y, py1)
+                ix2 = min(x + ow, px2)
+                iy2 = min(y + oh, py2)
+                
+                if ix2 > ix1 and iy2 > iy1:
+                    inter_area = (ix2 - ix1) * (iy2 - iy1)
+                    box_area = ow * oh
+                    p_area = (px2 - px1) * (py2 - py1)
+                    iou = inter_area / float(box_area + p_area - inter_area)
+                    if iou > 0.12:
+                        overlap = True
+                        break
+            
+            if not overlap:
+                bg.paste(obj_img, (x, y), obj_img)
+                placed_boxes.append((x, y, x + ow, y + oh))
+                x_center = (x + ow / 2.0) / width
+                y_center = (y + oh / 2.0) / height
+                w_norm = ow / float(width)
+                h_norm = oh / float(height)
+                annotations.append(f"{class_idx} {x_center:.6f} {y_center:.6f} {w_norm:.6f} {h_norm:.6f}")
+                placed = True
+                break
+                
+        if not placed and len(placed_boxes) == 0:
+            x = random.randint(30, width - ow - 30)
+            y = random.randint(30, height - oh - 30)
+            bg.paste(obj_img, (x, y), obj_img)
+            placed_boxes.append((x, y, x + ow, y + oh))
+            x_center = (x + ow / 2.0) / width
+            y_center = (y + oh / 2.0) / height
+            w_norm = ow / float(width)
+            h_norm = oh / float(height)
+            annotations.append(f"{class_idx} {x_center:.6f} {y_center:.6f} {w_norm:.6f} {h_norm:.6f}")
+            
+    bg = bg.filter(ImageFilter.GaussianBlur(0.5))
+    return bg, annotations
+
+def build_dataset(base_dir="dataset", train_count=200, val_count=50):
+    os.makedirs(os.path.join(base_dir, "images", "train"), exist_ok=True)
+    os.makedirs(os.path.join(base_dir, "images", "val"), exist_ok=True)
+    os.makedirs(os.path.join(base_dir, "labels", "train"), exist_ok=True)
+    os.makedirs(os.path.join(base_dir, "labels", "val"), exist_ok=True)
+    
+    with open(os.path.join(base_dir, "classes.txt"), "w") as f:
+        f.write("\n".join(CLASSES))
+        
+    print(f"Generating {train_count} training samples for {len(CLASSES)} classes...")
+    for idx in range(train_count):
+        img, ann = generate_sample()
+        img.save(os.path.join(base_dir, "images", "train", f"train_{idx:04d}.jpg"), quality=90)
+        with open(os.path.join(base_dir, "labels", "train", f"train_{idx:04d}.txt"), "w") as f:
+            f.write("\n".join(ann))
+            
+    print(f"Generating {val_count} validation samples...")
+    for idx in range(val_count):
+        img, ann = generate_sample()
+        img.save(os.path.join(base_dir, "images", "val", f"val_{idx:04d}.jpg"), quality=90)
+        with open(os.path.join(base_dir, "labels", "val", f"val_{idx:04d}.txt"), "w") as f:
+            f.write("\n".join(ann))
+            
+    print("Dataset generation complete!")
+
+if __name__ == "__main__":
+    build_dataset(train_count=200, val_count=50)
diff --git a/dataset.py b/dataset.py
new file mode 100644
index 0000000..85f824b
--- /dev/null
+++ b/dataset.py
@@ -0,0 +1,103 @@
+import os
+import cv2
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+class FruitVegetableDataset(Dataset):
+    def __init__(self, images_dir, labels_dir, transform=None):
+        """
+        Args:
+            images_dir (str): Path to images directory.
+            labels_dir (str): Path to labels directory.
+            transform (albumentations.Compose, optional): Albumentations transform pipeline.
+        """
+        self.images_dir = images_dir
+        self.labels_dir = labels_dir
+        self.transform = transform
+        
+        # List all image files
+        self.image_files = sorted([
+            f for f in os.listdir(images_dir) 
+            if f.lower().endswith(('.jpg', '.jpeg', '.png'))
+        ])
+
+    def __len__(self):
+        return len(self.image_files)
+
+    def __getitem__(self, idx):
+        img_name = self.image_files[idx]
+        img_path = os.path.join(self.images_dir, img_name)
+        
+        # Load image via OpenCV (BGR) and convert to RGB
+        image = cv2.imread(img_path)
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        height, width, _ = image.shape
+        
+        # Load corresponding label file
+        label_name = os.path.splitext(img_name)[0] + ".txt"
+        label_path = os.path.join(self.labels_dir, label_name)
+        
+        boxes = []
+        class_ids = []
+        
+        if os.path.exists(label_path):
+            with open(label_path, "r") as f:
+                for line in f:
+                    parts = line.strip().split()
+                    if len(parts) == 5:
+                        class_id = int(parts[0])
+                        # YOLO format: x_center, y_center, width, height (normalized)
+                        x_c, y_c, w, h = map(float, parts[1:])
+                        
+                        # Convert to Pascal VOC absolute coordinates [x_min, y_min, x_max, y_max]
+                        xmin = (x_c - w / 2.0) * width
+                        ymin = (y_c - h / 2.0) * height
+                        xmax = (x_c + w / 2.0) * width
+                        ymax = (y_c + h / 2.0) * height
+                        
+                        # Ensure coordinates are within image boundaries
+                        xmin = max(0.0, min(xmin, float(width - 1)))
+                        ymin = max(0.0, min(ymin, float(height - 1)))
+                        xmax = max(xmin + 1.0, min(xmax, float(width)))
+                        ymax = max(ymin + 1.0, min(ymax, float(height)))
+                        
+                        boxes.append([xmin, ymin, xmax, ymax])
+                        class_ids.append(class_id)
+                        
+        boxes = np.array(boxes, dtype=np.float32).reshape(-1, 4)
+        class_ids = np.array(class_ids, dtype=np.int64)
+        
+        # Apply Albumentations transformations
+        if self.transform:
+            augmented = self.transform(
+                image=image,
+                bboxes=boxes,
+                category_ids=class_ids
+            )
+            image = augmented["image"]
+            boxes = np.array(augmented["bboxes"], dtype=np.float32).reshape(-1, 4)
+            class_ids = np.array(augmented["category_ids"], dtype=np.int64)
+            
+        # Convert image to CHW tensor and scale to [0, 1]
+        image_tensor = torch.from_numpy(image).permute(2, 0, 1).float() / 255.0
+        
+        # Convert targets to PyTorch Tensors
+        # NOTE: torchvision models reserve class 0 for background.
+        # We must shift our class IDs by +1 (0 -> 1, 1 -> 2, etc.)
+        torch_boxes = torch.as_tensor(boxes, dtype=torch.float32)
+        torch_labels = torch.as_tensor(class_ids + 1, dtype=torch.int64)
+        
+        target = {
+            "boxes": torch_boxes,
+            "labels": torch_labels,
+            "image_id": torch.tensor([idx])
+        }
+        
+        return image_tensor, target
+
+def collate_fn(batch):
+    """
+    Collate function for DataLoader. Returns a tuple of (images, targets).
+    """
+    return tuple(zip(*batch))
diff --git a/dataset/classes.txt b/dataset/classes.txt
new file mode 100644
index 0000000..ae5d8b5
--- /dev/null
+++ b/dataset/classes.txt
@@ -0,0 +1,28 @@
+apfel_elstar
+apfel_gala
+apfel_granny_smith
+banane_chiquita
+banane_bio
+birne
+orange
+zitrone
+limette
+erdbeere
+blaubeere
+weintraube_hell
+weintraube_dunkel
+pfirsich
+tomate
+gurke
+kartoffel
+karotte
+zwiebel_gelb
+zwiebel_rot
+knoblauch
+brokkoli
+paprika_rot
+paprika_gelb
+paprika_gruen
+champignon
+zucchini
+avocado
\ No newline at end of file
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0023.txt b/dataset/labels/train/train_0023.txt
new file mode 100644
index 0000000..4a11def
--- /dev/null
+++ b/dataset/labels/train/train_0023.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0024.txt b/dataset/labels/train/train_0024.txt
new file mode 100644
index 0000000..0ca39be
--- /dev/null
+++ b/dataset/labels/train/train_0024.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0025.txt b/dataset/labels/train/train_0025.txt
new file mode 100644
index 0000000..9ba4593
--- /dev/null
+++ b/dataset/labels/train/train_0025.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0026.txt b/dataset/labels/train/train_0026.txt
new file mode 100644
index 0000000..735cd99
--- /dev/null
+++ b/dataset/labels/train/train_0026.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0027.txt b/dataset/labels/train/train_0027.txt
new file mode 100644
index 0000000..f8b501f
--- /dev/null
+++ b/dataset/labels/train/train_0027.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0028.txt b/dataset/labels/train/train_0028.txt
new file mode 100644
index 0000000..7d11be8
--- /dev/null
+++ b/dataset/labels/train/train_0028.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0029.txt b/dataset/labels/train/train_0029.txt
new file mode 100644
index 0000000..d5567c2
--- /dev/null
+++ b/dataset/labels/train/train_0029.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0030.txt b/dataset/labels/train/train_0030.txt
new file mode 100644
index 0000000..e7676c0
--- /dev/null
+++ b/dataset/labels/train/train_0030.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0031.txt b/dataset/labels/train/train_0031.txt
new file mode 100644
index 0000000..2a53eeb
--- /dev/null
+++ b/dataset/labels/train/train_0031.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0032.txt b/dataset/labels/train/train_0032.txt
new file mode 100644
index 0000000..79f0fbe
--- /dev/null
+++ b/dataset/labels/train/train_0032.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0033.txt b/dataset/labels/train/train_0033.txt
new file mode 100644
index 0000000..8800aee
--- /dev/null
+++ b/dataset/labels/train/train_0033.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0034.txt b/dataset/labels/train/train_0034.txt
new file mode 100644
index 0000000..51ebab4
--- /dev/null
+++ b/dataset/labels/train/train_0034.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0035.txt b/dataset/labels/train/train_0035.txt
new file mode 100644
index 0000000..db14627
--- /dev/null
+++ b/dataset/labels/train/train_0035.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0036.txt b/dataset/labels/train/train_0036.txt
new file mode 100644
index 0000000..f73f1d6
--- /dev/null
+++ b/dataset/labels/train/train_0036.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0037.txt b/dataset/labels/train/train_0037.txt
new file mode 100644
index 0000000..a7334de
--- /dev/null
+++ b/dataset/labels/train/train_0037.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0038.txt b/dataset/labels/train/train_0038.txt
new file mode 100644
index 0000000..99c1a35
--- /dev/null
+++ b/dataset/labels/train/train_0038.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0039.txt b/dataset/labels/train/train_0039.txt
new file mode 100644
index 0000000..8e740ab
--- /dev/null
+++ b/dataset/labels/train/train_0039.txt
@@ -0,0 +1,3 @@
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+5 0.580469 0.798958 0.170313 0.160417
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0040.txt b/dataset/labels/train/train_0040.txt
new file mode 100644
index 0000000..ac7b6fd
--- /dev/null
+++ b/dataset/labels/train/train_0040.txt
@@ -0,0 +1,3 @@
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+8 0.807813 0.203125 0.181250 0.164583
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0041.txt b/dataset/labels/train/train_0041.txt
new file mode 100644
index 0000000..6bf16b7
--- /dev/null
+++ b/dataset/labels/train/train_0041.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0042.txt b/dataset/labels/train/train_0042.txt
new file mode 100644
index 0000000..44ef9da
--- /dev/null
+++ b/dataset/labels/train/train_0042.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0043.txt b/dataset/labels/train/train_0043.txt
new file mode 100644
index 0000000..a906225
--- /dev/null
+++ b/dataset/labels/train/train_0043.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0044.txt b/dataset/labels/train/train_0044.txt
new file mode 100644
index 0000000..09c86c3
--- /dev/null
+++ b/dataset/labels/train/train_0044.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0045.txt b/dataset/labels/train/train_0045.txt
new file mode 100644
index 0000000..fa1a8e1
--- /dev/null
+++ b/dataset/labels/train/train_0045.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0046.txt b/dataset/labels/train/train_0046.txt
new file mode 100644
index 0000000..519309a
--- /dev/null
+++ b/dataset/labels/train/train_0046.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0047.txt b/dataset/labels/train/train_0047.txt
new file mode 100644
index 0000000..3a053d9
--- /dev/null
+++ b/dataset/labels/train/train_0047.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0048.txt b/dataset/labels/train/train_0048.txt
new file mode 100644
index 0000000..53d2c70
--- /dev/null
+++ b/dataset/labels/train/train_0048.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0049.txt b/dataset/labels/train/train_0049.txt
new file mode 100644
index 0000000..06abfc8
--- /dev/null
+++ b/dataset/labels/train/train_0049.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0050.txt b/dataset/labels/train/train_0050.txt
new file mode 100644
index 0000000..917918c
--- /dev/null
+++ b/dataset/labels/train/train_0050.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0051.txt b/dataset/labels/train/train_0051.txt
new file mode 100644
index 0000000..421fbb4
--- /dev/null
+++ b/dataset/labels/train/train_0051.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0052.txt b/dataset/labels/train/train_0052.txt
new file mode 100644
index 0000000..cdf2430
--- /dev/null
+++ b/dataset/labels/train/train_0052.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0053.txt b/dataset/labels/train/train_0053.txt
new file mode 100644
index 0000000..fdedfef
--- /dev/null
+++ b/dataset/labels/train/train_0053.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0054.txt b/dataset/labels/train/train_0054.txt
new file mode 100644
index 0000000..4f30bcc
--- /dev/null
+++ b/dataset/labels/train/train_0054.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0055.txt b/dataset/labels/train/train_0055.txt
new file mode 100644
index 0000000..c759969
--- /dev/null
+++ b/dataset/labels/train/train_0055.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0056.txt b/dataset/labels/train/train_0056.txt
new file mode 100644
index 0000000..d386c3c
--- /dev/null
+++ b/dataset/labels/train/train_0056.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0057.txt b/dataset/labels/train/train_0057.txt
new file mode 100644
index 0000000..4263017
--- /dev/null
+++ b/dataset/labels/train/train_0057.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0058.txt b/dataset/labels/train/train_0058.txt
new file mode 100644
index 0000000..f3380c2
--- /dev/null
+++ b/dataset/labels/train/train_0058.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0059.txt b/dataset/labels/train/train_0059.txt
new file mode 100644
index 0000000..e5cb824
--- /dev/null
+++ b/dataset/labels/train/train_0059.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0060.txt b/dataset/labels/train/train_0060.txt
new file mode 100644
index 0000000..e4d6267
--- /dev/null
+++ b/dataset/labels/train/train_0060.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0061.txt b/dataset/labels/train/train_0061.txt
new file mode 100644
index 0000000..8837a6f
--- /dev/null
+++ b/dataset/labels/train/train_0061.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0062.txt b/dataset/labels/train/train_0062.txt
new file mode 100644
index 0000000..cf0ce90
--- /dev/null
+++ b/dataset/labels/train/train_0062.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0063.txt b/dataset/labels/train/train_0063.txt
new file mode 100644
index 0000000..b8a17ab
--- /dev/null
+++ b/dataset/labels/train/train_0063.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0064.txt b/dataset/labels/train/train_0064.txt
new file mode 100644
index 0000000..0f0a8c8
--- /dev/null
+++ b/dataset/labels/train/train_0064.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0065.txt b/dataset/labels/train/train_0065.txt
new file mode 100644
index 0000000..9b66ae1
--- /dev/null
+++ b/dataset/labels/train/train_0065.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0066.txt b/dataset/labels/train/train_0066.txt
new file mode 100644
index 0000000..bb125d0
--- /dev/null
+++ b/dataset/labels/train/train_0066.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0067.txt b/dataset/labels/train/train_0067.txt
new file mode 100644
index 0000000..eec6772
--- /dev/null
+++ b/dataset/labels/train/train_0067.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0068.txt b/dataset/labels/train/train_0068.txt
new file mode 100644
index 0000000..c2e43a9
--- /dev/null
+++ b/dataset/labels/train/train_0068.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0069.txt b/dataset/labels/train/train_0069.txt
new file mode 100644
index 0000000..60d0f85
--- /dev/null
+++ b/dataset/labels/train/train_0069.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0070.txt b/dataset/labels/train/train_0070.txt
new file mode 100644
index 0000000..d447267
--- /dev/null
+++ b/dataset/labels/train/train_0070.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0071.txt b/dataset/labels/train/train_0071.txt
new file mode 100644
index 0000000..ab496dd
--- /dev/null
+++ b/dataset/labels/train/train_0071.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0072.txt b/dataset/labels/train/train_0072.txt
new file mode 100644
index 0000000..83a7e7e
--- /dev/null
+++ b/dataset/labels/train/train_0072.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0073.txt b/dataset/labels/train/train_0073.txt
new file mode 100644
index 0000000..037fef8
--- /dev/null
+++ b/dataset/labels/train/train_0073.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0074.txt b/dataset/labels/train/train_0074.txt
new file mode 100644
index 0000000..0fd73ce
--- /dev/null
+++ b/dataset/labels/train/train_0074.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0075.txt b/dataset/labels/train/train_0075.txt
new file mode 100644
index 0000000..6bb0dbe
--- /dev/null
+++ b/dataset/labels/train/train_0075.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0076.txt b/dataset/labels/train/train_0076.txt
new file mode 100644
index 0000000..0205552
--- /dev/null
+++ b/dataset/labels/train/train_0076.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0077.txt b/dataset/labels/train/train_0077.txt
new file mode 100644
index 0000000..2a4ac33
--- /dev/null
+++ b/dataset/labels/train/train_0077.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0078.txt b/dataset/labels/train/train_0078.txt
new file mode 100644
index 0000000..88b199c
--- /dev/null
+++ b/dataset/labels/train/train_0078.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0079.txt b/dataset/labels/train/train_0079.txt
new file mode 100644
index 0000000..7f2132e
--- /dev/null
+++ b/dataset/labels/train/train_0079.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0080.txt b/dataset/labels/train/train_0080.txt
new file mode 100644
index 0000000..78805e1
--- /dev/null
+++ b/dataset/labels/train/train_0080.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0081.txt b/dataset/labels/train/train_0081.txt
new file mode 100644
index 0000000..d81f8c0
--- /dev/null
+++ b/dataset/labels/train/train_0081.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0082.txt b/dataset/labels/train/train_0082.txt
new file mode 100644
index 0000000..dd79fa7
--- /dev/null
+++ b/dataset/labels/train/train_0082.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0083.txt b/dataset/labels/train/train_0083.txt
new file mode 100644
index 0000000..57bc64c
--- /dev/null
+++ b/dataset/labels/train/train_0083.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0084.txt b/dataset/labels/train/train_0084.txt
new file mode 100644
index 0000000..28f490d
--- /dev/null
+++ b/dataset/labels/train/train_0084.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0085.txt b/dataset/labels/train/train_0085.txt
new file mode 100644
index 0000000..fc1ba9b
--- /dev/null
+++ b/dataset/labels/train/train_0085.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0086.txt b/dataset/labels/train/train_0086.txt
new file mode 100644
index 0000000..e98a0c1
--- /dev/null
+++ b/dataset/labels/train/train_0086.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0087.txt b/dataset/labels/train/train_0087.txt
new file mode 100644
index 0000000..9bea74c
--- /dev/null
+++ b/dataset/labels/train/train_0087.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0088.txt b/dataset/labels/train/train_0088.txt
new file mode 100644
index 0000000..27f2b3d
--- /dev/null
+++ b/dataset/labels/train/train_0088.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0089.txt b/dataset/labels/train/train_0089.txt
new file mode 100644
index 0000000..496f118
--- /dev/null
+++ b/dataset/labels/train/train_0089.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0090.txt b/dataset/labels/train/train_0090.txt
new file mode 100644
index 0000000..b84a862
--- /dev/null
+++ b/dataset/labels/train/train_0090.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0091.txt b/dataset/labels/train/train_0091.txt
new file mode 100644
index 0000000..123c158
--- /dev/null
+++ b/dataset/labels/train/train_0091.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0092.txt b/dataset/labels/train/train_0092.txt
new file mode 100644
index 0000000..f3afeea
--- /dev/null
+++ b/dataset/labels/train/train_0092.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0093.txt b/dataset/labels/train/train_0093.txt
new file mode 100644
index 0000000..17d47bd
--- /dev/null
+++ b/dataset/labels/train/train_0093.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0094.txt b/dataset/labels/train/train_0094.txt
new file mode 100644
index 0000000..b1c358b
--- /dev/null
+++ b/dataset/labels/train/train_0094.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0095.txt b/dataset/labels/train/train_0095.txt
new file mode 100644
index 0000000..a78de0f
--- /dev/null
+++ b/dataset/labels/train/train_0095.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0096.txt b/dataset/labels/train/train_0096.txt
new file mode 100644
index 0000000..27d509c
--- /dev/null
+++ b/dataset/labels/train/train_0096.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0097.txt b/dataset/labels/train/train_0097.txt
new file mode 100644
index 0000000..c5703b6
--- /dev/null
+++ b/dataset/labels/train/train_0097.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0098.txt b/dataset/labels/train/train_0098.txt
new file mode 100644
index 0000000..ff10b1c
--- /dev/null
+++ b/dataset/labels/train/train_0098.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0099.txt b/dataset/labels/train/train_0099.txt
new file mode 100644
index 0000000..b25e813
--- /dev/null
+++ b/dataset/labels/train/train_0099.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0100.txt b/dataset/labels/train/train_0100.txt
new file mode 100644
index 0000000..116962f
--- /dev/null
+++ b/dataset/labels/train/train_0100.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0101.txt b/dataset/labels/train/train_0101.txt
new file mode 100644
index 0000000..342c0b5
--- /dev/null
+++ b/dataset/labels/train/train_0101.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0102.txt b/dataset/labels/train/train_0102.txt
new file mode 100644
index 0000000..897862f
--- /dev/null
+++ b/dataset/labels/train/train_0102.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0103.txt b/dataset/labels/train/train_0103.txt
new file mode 100644
index 0000000..4c2c69b
--- /dev/null
+++ b/dataset/labels/train/train_0103.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0104.txt b/dataset/labels/train/train_0104.txt
new file mode 100644
index 0000000..c8f54b7
--- /dev/null
+++ b/dataset/labels/train/train_0104.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0105.txt b/dataset/labels/train/train_0105.txt
new file mode 100644
index 0000000..888a5a1
--- /dev/null
+++ b/dataset/labels/train/train_0105.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0106.txt b/dataset/labels/train/train_0106.txt
new file mode 100644
index 0000000..8a7b0e6
--- /dev/null
+++ b/dataset/labels/train/train_0106.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0107.txt b/dataset/labels/train/train_0107.txt
new file mode 100644
index 0000000..5239e12
--- /dev/null
+++ b/dataset/labels/train/train_0107.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0108.txt b/dataset/labels/train/train_0108.txt
new file mode 100644
index 0000000..a7f680e
--- /dev/null
+++ b/dataset/labels/train/train_0108.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0109.txt b/dataset/labels/train/train_0109.txt
new file mode 100644
index 0000000..d1570a9
--- /dev/null
+++ b/dataset/labels/train/train_0109.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0110.txt b/dataset/labels/train/train_0110.txt
new file mode 100644
index 0000000..e7fd336
--- /dev/null
+++ b/dataset/labels/train/train_0110.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0111.txt b/dataset/labels/train/train_0111.txt
new file mode 100644
index 0000000..3479b41
--- /dev/null
+++ b/dataset/labels/train/train_0111.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0112.txt b/dataset/labels/train/train_0112.txt
new file mode 100644
index 0000000..d441c50
--- /dev/null
+++ b/dataset/labels/train/train_0112.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0113.txt b/dataset/labels/train/train_0113.txt
new file mode 100644
index 0000000..25bf8ba
--- /dev/null
+++ b/dataset/labels/train/train_0113.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0114.txt b/dataset/labels/train/train_0114.txt
new file mode 100644
index 0000000..3977b50
--- /dev/null
+++ b/dataset/labels/train/train_0114.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0115.txt b/dataset/labels/train/train_0115.txt
new file mode 100644
index 0000000..a5d5d66
--- /dev/null
+++ b/dataset/labels/train/train_0115.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0116.txt b/dataset/labels/train/train_0116.txt
new file mode 100644
index 0000000..80bce0a
--- /dev/null
+++ b/dataset/labels/train/train_0116.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0117.txt b/dataset/labels/train/train_0117.txt
new file mode 100644
index 0000000..ae9157d
--- /dev/null
+++ b/dataset/labels/train/train_0117.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0118.txt b/dataset/labels/train/train_0118.txt
new file mode 100644
index 0000000..62e6ed3
--- /dev/null
+++ b/dataset/labels/train/train_0118.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0119.txt b/dataset/labels/train/train_0119.txt
new file mode 100644
index 0000000..d67f374
--- /dev/null
+++ b/dataset/labels/train/train_0119.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0120.txt b/dataset/labels/train/train_0120.txt
new file mode 100644
index 0000000..2d66ad5
--- /dev/null
+++ b/dataset/labels/train/train_0120.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0121.txt b/dataset/labels/train/train_0121.txt
new file mode 100644
index 0000000..24e5e73
--- /dev/null
+++ b/dataset/labels/train/train_0121.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0122.txt b/dataset/labels/train/train_0122.txt
new file mode 100644
index 0000000..e3d79a5
--- /dev/null
+++ b/dataset/labels/train/train_0122.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0123.txt b/dataset/labels/train/train_0123.txt
new file mode 100644
index 0000000..cee530f
--- /dev/null
+++ b/dataset/labels/train/train_0123.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0124.txt b/dataset/labels/train/train_0124.txt
new file mode 100644
index 0000000..8d3fa50
--- /dev/null
+++ b/dataset/labels/train/train_0124.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0125.txt b/dataset/labels/train/train_0125.txt
new file mode 100644
index 0000000..695b0a5
--- /dev/null
+++ b/dataset/labels/train/train_0125.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0126.txt b/dataset/labels/train/train_0126.txt
new file mode 100644
index 0000000..b79a45c
--- /dev/null
+++ b/dataset/labels/train/train_0126.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0127.txt b/dataset/labels/train/train_0127.txt
new file mode 100644
index 0000000..cf0379a
--- /dev/null
+++ b/dataset/labels/train/train_0127.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0128.txt b/dataset/labels/train/train_0128.txt
new file mode 100644
index 0000000..bccc7fc
--- /dev/null
+++ b/dataset/labels/train/train_0128.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0129.txt b/dataset/labels/train/train_0129.txt
new file mode 100644
index 0000000..a437f16
--- /dev/null
+++ b/dataset/labels/train/train_0129.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0130.txt b/dataset/labels/train/train_0130.txt
new file mode 100644
index 0000000..e4758b5
--- /dev/null
+++ b/dataset/labels/train/train_0130.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0131.txt b/dataset/labels/train/train_0131.txt
new file mode 100644
index 0000000..585b574
--- /dev/null
+++ b/dataset/labels/train/train_0131.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0132.txt b/dataset/labels/train/train_0132.txt
new file mode 100644
index 0000000..64f7a18
--- /dev/null
+++ b/dataset/labels/train/train_0132.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0133.txt b/dataset/labels/train/train_0133.txt
new file mode 100644
index 0000000..3e94cd8
--- /dev/null
+++ b/dataset/labels/train/train_0133.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0134.txt b/dataset/labels/train/train_0134.txt
new file mode 100644
index 0000000..c68ff4f
--- /dev/null
+++ b/dataset/labels/train/train_0134.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0135.txt b/dataset/labels/train/train_0135.txt
new file mode 100644
index 0000000..80eee15
--- /dev/null
+++ b/dataset/labels/train/train_0135.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0136.txt b/dataset/labels/train/train_0136.txt
new file mode 100644
index 0000000..7de2450
--- /dev/null
+++ b/dataset/labels/train/train_0136.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0137.txt b/dataset/labels/train/train_0137.txt
new file mode 100644
index 0000000..24dc69b
--- /dev/null
+++ b/dataset/labels/train/train_0137.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0138.txt b/dataset/labels/train/train_0138.txt
new file mode 100644
index 0000000..a8ef181
--- /dev/null
+++ b/dataset/labels/train/train_0138.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0139.txt b/dataset/labels/train/train_0139.txt
new file mode 100644
index 0000000..b49bb51
--- /dev/null
+++ b/dataset/labels/train/train_0139.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0140.txt b/dataset/labels/train/train_0140.txt
new file mode 100644
index 0000000..54d2c65
--- /dev/null
+++ b/dataset/labels/train/train_0140.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0141.txt b/dataset/labels/train/train_0141.txt
new file mode 100644
index 0000000..3854cbf
--- /dev/null
+++ b/dataset/labels/train/train_0141.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0142.txt b/dataset/labels/train/train_0142.txt
new file mode 100644
index 0000000..3cb74ab
--- /dev/null
+++ b/dataset/labels/train/train_0142.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0143.txt b/dataset/labels/train/train_0143.txt
new file mode 100644
index 0000000..5fb3c4f
--- /dev/null
+++ b/dataset/labels/train/train_0143.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0144.txt b/dataset/labels/train/train_0144.txt
new file mode 100644
index 0000000..8568ade
--- /dev/null
+++ b/dataset/labels/train/train_0144.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0145.txt b/dataset/labels/train/train_0145.txt
new file mode 100644
index 0000000..4372a0d
--- /dev/null
+++ b/dataset/labels/train/train_0145.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0146.txt b/dataset/labels/train/train_0146.txt
new file mode 100644
index 0000000..7f2032b
--- /dev/null
+++ b/dataset/labels/train/train_0146.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0147.txt b/dataset/labels/train/train_0147.txt
new file mode 100644
index 0000000..fdef0d3
--- /dev/null
+++ b/dataset/labels/train/train_0147.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0148.txt b/dataset/labels/train/train_0148.txt
new file mode 100644
index 0000000..780895e
--- /dev/null
+++ b/dataset/labels/train/train_0148.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0149.txt b/dataset/labels/train/train_0149.txt
new file mode 100644
index 0000000..b10380b
--- /dev/null
+++ b/dataset/labels/train/train_0149.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0150.txt b/dataset/labels/train/train_0150.txt
new file mode 100644
index 0000000..3af8972
--- /dev/null
+++ b/dataset/labels/train/train_0150.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0151.txt b/dataset/labels/train/train_0151.txt
new file mode 100644
index 0000000..d4bdda7
--- /dev/null
+++ b/dataset/labels/train/train_0151.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0152.txt b/dataset/labels/train/train_0152.txt
new file mode 100644
index 0000000..2303626
--- /dev/null
+++ b/dataset/labels/train/train_0152.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0153.txt b/dataset/labels/train/train_0153.txt
new file mode 100644
index 0000000..6318e71
--- /dev/null
+++ b/dataset/labels/train/train_0153.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0154.txt b/dataset/labels/train/train_0154.txt
new file mode 100644
index 0000000..d49d91d
--- /dev/null
+++ b/dataset/labels/train/train_0154.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0155.txt b/dataset/labels/train/train_0155.txt
new file mode 100644
index 0000000..e14cb1f
--- /dev/null
+++ b/dataset/labels/train/train_0155.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0156.txt b/dataset/labels/train/train_0156.txt
new file mode 100644
index 0000000..d0995a9
--- /dev/null
+++ b/dataset/labels/train/train_0156.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0157.txt b/dataset/labels/train/train_0157.txt
new file mode 100644
index 0000000..ae0747b
--- /dev/null
+++ b/dataset/labels/train/train_0157.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0158.txt b/dataset/labels/train/train_0158.txt
new file mode 100644
index 0000000..bbf8981
--- /dev/null
+++ b/dataset/labels/train/train_0158.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0159.txt b/dataset/labels/train/train_0159.txt
new file mode 100644
index 0000000..8122e19
--- /dev/null
+++ b/dataset/labels/train/train_0159.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0160.txt b/dataset/labels/train/train_0160.txt
new file mode 100644
index 0000000..9c03b2a
--- /dev/null
+++ b/dataset/labels/train/train_0160.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0161.txt b/dataset/labels/train/train_0161.txt
new file mode 100644
index 0000000..f0c5527
--- /dev/null
+++ b/dataset/labels/train/train_0161.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0162.txt b/dataset/labels/train/train_0162.txt
new file mode 100644
index 0000000..7aa10e6
--- /dev/null
+++ b/dataset/labels/train/train_0162.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0163.txt b/dataset/labels/train/train_0163.txt
new file mode 100644
index 0000000..c99f3c1
--- /dev/null
+++ b/dataset/labels/train/train_0163.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0164.txt b/dataset/labels/train/train_0164.txt
new file mode 100644
index 0000000..e03dfb4
--- /dev/null
+++ b/dataset/labels/train/train_0164.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0165.txt b/dataset/labels/train/train_0165.txt
new file mode 100644
index 0000000..a48bbbf
--- /dev/null
+++ b/dataset/labels/train/train_0165.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0166.txt b/dataset/labels/train/train_0166.txt
new file mode 100644
index 0000000..34a84a8
--- /dev/null
+++ b/dataset/labels/train/train_0166.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0167.txt b/dataset/labels/train/train_0167.txt
new file mode 100644
index 0000000..6474531
--- /dev/null
+++ b/dataset/labels/train/train_0167.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0168.txt b/dataset/labels/train/train_0168.txt
new file mode 100644
index 0000000..384f896
--- /dev/null
+++ b/dataset/labels/train/train_0168.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0169.txt b/dataset/labels/train/train_0169.txt
new file mode 100644
index 0000000..aaf0332
--- /dev/null
+++ b/dataset/labels/train/train_0169.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0170.txt b/dataset/labels/train/train_0170.txt
new file mode 100644
index 0000000..19c589f
--- /dev/null
+++ b/dataset/labels/train/train_0170.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0171.txt b/dataset/labels/train/train_0171.txt
new file mode 100644
index 0000000..f10d628
--- /dev/null
+++ b/dataset/labels/train/train_0171.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0172.txt b/dataset/labels/train/train_0172.txt
new file mode 100644
index 0000000..ddfc70a
--- /dev/null
+++ b/dataset/labels/train/train_0172.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0173.txt b/dataset/labels/train/train_0173.txt
new file mode 100644
index 0000000..b5114fd
--- /dev/null
+++ b/dataset/labels/train/train_0173.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0174.txt b/dataset/labels/train/train_0174.txt
new file mode 100644
index 0000000..ac082c7
--- /dev/null
+++ b/dataset/labels/train/train_0174.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0175.txt b/dataset/labels/train/train_0175.txt
new file mode 100644
index 0000000..e17efcf
--- /dev/null
+++ b/dataset/labels/train/train_0175.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0176.txt b/dataset/labels/train/train_0176.txt
new file mode 100644
index 0000000..067cf4e
--- /dev/null
+++ b/dataset/labels/train/train_0176.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0177.txt b/dataset/labels/train/train_0177.txt
new file mode 100644
index 0000000..42ab06b
--- /dev/null
+++ b/dataset/labels/train/train_0177.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0178.txt b/dataset/labels/train/train_0178.txt
new file mode 100644
index 0000000..db18d40
--- /dev/null
+++ b/dataset/labels/train/train_0178.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0179.txt b/dataset/labels/train/train_0179.txt
new file mode 100644
index 0000000..0a9e42a
--- /dev/null
+++ b/dataset/labels/train/train_0179.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0180.txt b/dataset/labels/train/train_0180.txt
new file mode 100644
index 0000000..aec4c7b
--- /dev/null
+++ b/dataset/labels/train/train_0180.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0181.txt b/dataset/labels/train/train_0181.txt
new file mode 100644
index 0000000..fa440ca
--- /dev/null
+++ b/dataset/labels/train/train_0181.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0182.txt b/dataset/labels/train/train_0182.txt
new file mode 100644
index 0000000..5097f66
--- /dev/null
+++ b/dataset/labels/train/train_0182.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0183.txt b/dataset/labels/train/train_0183.txt
new file mode 100644
index 0000000..fe68c80
--- /dev/null
+++ b/dataset/labels/train/train_0183.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0184.txt b/dataset/labels/train/train_0184.txt
new file mode 100644
index 0000000..6c6d9e6
--- /dev/null
+++ b/dataset/labels/train/train_0184.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0185.txt b/dataset/labels/train/train_0185.txt
new file mode 100644
index 0000000..d26657f
--- /dev/null
+++ b/dataset/labels/train/train_0185.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0186.txt b/dataset/labels/train/train_0186.txt
new file mode 100644
index 0000000..acec2da
--- /dev/null
+++ b/dataset/labels/train/train_0186.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0187.txt b/dataset/labels/train/train_0187.txt
new file mode 100644
index 0000000..a05a706
--- /dev/null
+++ b/dataset/labels/train/train_0187.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0188.txt b/dataset/labels/train/train_0188.txt
new file mode 100644
index 0000000..95b375f
--- /dev/null
+++ b/dataset/labels/train/train_0188.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0189.txt b/dataset/labels/train/train_0189.txt
new file mode 100644
index 0000000..eb4910f
--- /dev/null
+++ b/dataset/labels/train/train_0189.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0190.txt b/dataset/labels/train/train_0190.txt
new file mode 100644
index 0000000..a0b7d3e
--- /dev/null
+++ b/dataset/labels/train/train_0190.txt
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0191.txt b/dataset/labels/train/train_0191.txt
new file mode 100644
index 0000000..a88a95e
--- /dev/null
+++ b/dataset/labels/train/train_0191.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0192.txt b/dataset/labels/train/train_0192.txt
new file mode 100644
index 0000000..1d09ef8
--- /dev/null
+++ b/dataset/labels/train/train_0192.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0193.txt b/dataset/labels/train/train_0193.txt
new file mode 100644
index 0000000..7d88e9a
--- /dev/null
+++ b/dataset/labels/train/train_0193.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0194.txt b/dataset/labels/train/train_0194.txt
new file mode 100644
index 0000000..6d020d6
--- /dev/null
+++ b/dataset/labels/train/train_0194.txt
@@ -0,0 +1,3 @@
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\ No newline at end of file
diff --git a/dataset/labels/train/train_0195.txt b/dataset/labels/train/train_0195.txt
new file mode 100644
index 0000000..96bc893
--- /dev/null
+++ b/dataset/labels/train/train_0195.txt
@@ -0,0 +1,2 @@
+3 0.574219 0.453125 0.176563 0.281250
+25 0.450781 0.791667 0.198437 0.237500
\ No newline at end of file
diff --git a/dataset/labels/train/train_0196.txt b/dataset/labels/train/train_0196.txt
new file mode 100644
index 0000000..d7ff5df
--- /dev/null
+++ b/dataset/labels/train/train_0196.txt
@@ -0,0 +1 @@
+4 0.692969 0.736458 0.129688 0.231250
\ No newline at end of file
diff --git a/dataset/labels/train/train_0197.txt b/dataset/labels/train/train_0197.txt
new file mode 100644
index 0000000..619acaa
--- /dev/null
+++ b/dataset/labels/train/train_0197.txt
@@ -0,0 +1,3 @@
+26 0.673438 0.818750 0.218750 0.133333
+26 0.784375 0.614583 0.068750 0.212500
+6 0.575000 0.525000 0.175000 0.233333
\ No newline at end of file
diff --git a/dataset/labels/train/train_0198.txt b/dataset/labels/train/train_0198.txt
new file mode 100644
index 0000000..903c7ef
--- /dev/null
+++ b/dataset/labels/train/train_0198.txt
@@ -0,0 +1,3 @@
+8 0.184375 0.150000 0.184375 0.166667
+2 0.434375 0.640625 0.150000 0.222917
+20 0.877344 0.553125 0.123438 0.193750
\ No newline at end of file
diff --git a/dataset/labels/train/train_0199.txt b/dataset/labels/train/train_0199.txt
new file mode 100644
index 0000000..9948f28
--- /dev/null
+++ b/dataset/labels/train/train_0199.txt
@@ -0,0 +1,2 @@
+18 0.391406 0.548958 0.117188 0.185417
+13 0.478906 0.720833 0.167187 0.191667
\ No newline at end of file
diff --git a/dataset/labels/train/user_capture_1780003783840.txt b/dataset/labels/train/user_capture_1780003783840.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780003783840.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780003803459.txt b/dataset/labels/train/user_capture_1780003803459.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780003803459.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780003806546.txt b/dataset/labels/train/user_capture_1780003806546.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780003806546.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780003809723.txt b/dataset/labels/train/user_capture_1780003809723.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780003809723.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780003812605.txt b/dataset/labels/train/user_capture_1780003812605.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780003812605.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780003815582.txt b/dataset/labels/train/user_capture_1780003815582.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780003815582.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780003818633.txt b/dataset/labels/train/user_capture_1780003818633.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780003818633.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780003821623.txt b/dataset/labels/train/user_capture_1780003821623.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780003821623.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780003824834.txt b/dataset/labels/train/user_capture_1780003824834.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780003824834.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780003828388.txt b/dataset/labels/train/user_capture_1780003828388.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780003828388.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780003830953.txt b/dataset/labels/train/user_capture_1780003830953.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780003830953.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780003833933.txt b/dataset/labels/train/user_capture_1780003833933.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780003833933.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780003836384.txt b/dataset/labels/train/user_capture_1780003836384.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780003836384.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780003838945.txt b/dataset/labels/train/user_capture_1780003838945.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780003838945.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780003841632.txt b/dataset/labels/train/user_capture_1780003841632.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780003841632.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780004908498.txt b/dataset/labels/train/user_capture_1780004908498.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780004908498.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780007172322.txt b/dataset/labels/train/user_capture_1780007172322.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780007172322.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780007176250.txt b/dataset/labels/train/user_capture_1780007176250.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780007176250.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780007179850.txt b/dataset/labels/train/user_capture_1780007179850.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780007179850.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780007182800.txt b/dataset/labels/train/user_capture_1780007182800.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780007182800.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780007185911.txt b/dataset/labels/train/user_capture_1780007185911.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780007185911.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780007193199.txt b/dataset/labels/train/user_capture_1780007193199.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780007193199.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780007197515.txt b/dataset/labels/train/user_capture_1780007197515.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780007197515.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780007201191.txt b/dataset/labels/train/user_capture_1780007201191.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780007201191.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/train/user_capture_1780007204503.txt b/dataset/labels/train/user_capture_1780007204503.txt
new file mode 100644
index 0000000..e6f0974
--- /dev/null
+++ b/dataset/labels/train/user_capture_1780007204503.txt
@@ -0,0 +1 @@
+16 0.500000 0.500000 0.650000 0.650000
diff --git a/dataset/labels/val/val_0000.txt b/dataset/labels/val/val_0000.txt
new file mode 100644
index 0000000..2bc3cfe
--- /dev/null
+++ b/dataset/labels/val/val_0000.txt
@@ -0,0 +1 @@
+27 0.839063 0.261458 0.140625 0.197917
\ No newline at end of file
diff --git a/dataset/labels/val/val_0001.txt b/dataset/labels/val/val_0001.txt
new file mode 100644
index 0000000..a1f5c45
--- /dev/null
+++ b/dataset/labels/val/val_0001.txt
@@ -0,0 +1 @@
+0 0.746094 0.427083 0.207813 0.262500
\ No newline at end of file
diff --git a/dataset/labels/val/val_0002.txt b/dataset/labels/val/val_0002.txt
new file mode 100644
index 0000000..cd6258a
--- /dev/null
+++ b/dataset/labels/val/val_0002.txt
@@ -0,0 +1,2 @@
+21 0.179688 0.613542 0.140625 0.277083
+16 0.817969 0.652083 0.151562 0.183333
\ No newline at end of file
diff --git a/dataset/labels/val/val_0003.txt b/dataset/labels/val/val_0003.txt
new file mode 100644
index 0000000..327623b
--- /dev/null
+++ b/dataset/labels/val/val_0003.txt
@@ -0,0 +1,2 @@
+15 0.740625 0.337500 0.178125 0.083333
+0 0.182031 0.525000 0.210938 0.320833
\ No newline at end of file
diff --git a/dataset/labels/val/val_0004.txt b/dataset/labels/val/val_0004.txt
new file mode 100644
index 0000000..6ad255c
--- /dev/null
+++ b/dataset/labels/val/val_0004.txt
@@ -0,0 +1 @@
+26 0.582812 0.751042 0.112500 0.343750
\ No newline at end of file
diff --git a/dataset/labels/val/val_0005.txt b/dataset/labels/val/val_0005.txt
new file mode 100644
index 0000000..9309f25
--- /dev/null
+++ b/dataset/labels/val/val_0005.txt
@@ -0,0 +1 @@
+17 0.313281 0.689583 0.176563 0.241667
\ No newline at end of file
diff --git a/dataset/labels/val/val_0006.txt b/dataset/labels/val/val_0006.txt
new file mode 100644
index 0000000..61cf078
--- /dev/null
+++ b/dataset/labels/val/val_0006.txt
@@ -0,0 +1,2 @@
+19 0.804688 0.643750 0.153125 0.237500
+11 0.635938 0.223958 0.218750 0.202083
\ No newline at end of file
diff --git a/dataset/labels/val/val_0007.txt b/dataset/labels/val/val_0007.txt
new file mode 100644
index 0000000..4a20abf
--- /dev/null
+++ b/dataset/labels/val/val_0007.txt
@@ -0,0 +1 @@
+23 0.666406 0.457292 0.132812 0.193750
\ No newline at end of file
diff --git a/dataset/labels/val/val_0008.txt b/dataset/labels/val/val_0008.txt
new file mode 100644
index 0000000..ccfbdf4
--- /dev/null
+++ b/dataset/labels/val/val_0008.txt
@@ -0,0 +1,2 @@
+23 0.738281 0.818750 0.160938 0.200000
+16 0.403906 0.479167 0.173437 0.212500
\ No newline at end of file
diff --git a/dataset/labels/val/val_0009.txt b/dataset/labels/val/val_0009.txt
new file mode 100644
index 0000000..d181618
--- /dev/null
+++ b/dataset/labels/val/val_0009.txt
@@ -0,0 +1,2 @@
+24 0.661719 0.672917 0.207813 0.270833
+21 0.142969 0.652083 0.101562 0.179167
\ No newline at end of file
diff --git a/dataset/labels/val/val_0010.txt b/dataset/labels/val/val_0010.txt
new file mode 100644
index 0000000..5959966
--- /dev/null
+++ b/dataset/labels/val/val_0010.txt
@@ -0,0 +1,3 @@
+18 0.617969 0.529167 0.126562 0.195833
+2 0.221875 0.439583 0.196875 0.258333
+6 0.435156 0.769792 0.192188 0.256250
\ No newline at end of file
diff --git a/dataset/labels/val/val_0011.txt b/dataset/labels/val/val_0011.txt
new file mode 100644
index 0000000..7293207
--- /dev/null
+++ b/dataset/labels/val/val_0011.txt
@@ -0,0 +1 @@
+13 0.246094 0.230208 0.148438 0.193750
\ No newline at end of file
diff --git a/dataset/labels/val/val_0012.txt b/dataset/labels/val/val_0012.txt
new file mode 100644
index 0000000..fe4b502
--- /dev/null
+++ b/dataset/labels/val/val_0012.txt
@@ -0,0 +1,2 @@
+21 0.839063 0.648958 0.218750 0.306250
+14 0.769531 0.258333 0.182812 0.204167
\ No newline at end of file
diff --git a/dataset/labels/val/val_0013.txt b/dataset/labels/val/val_0013.txt
new file mode 100644
index 0000000..072700d
--- /dev/null
+++ b/dataset/labels/val/val_0013.txt
@@ -0,0 +1,2 @@
+8 0.706250 0.448958 0.128125 0.106250
+2 0.444531 0.559375 0.210938 0.331250
\ No newline at end of file
diff --git a/dataset/labels/val/val_0014.txt b/dataset/labels/val/val_0014.txt
new file mode 100644
index 0000000..6f28607
--- /dev/null
+++ b/dataset/labels/val/val_0014.txt
@@ -0,0 +1,3 @@
+17 0.260156 0.737500 0.089063 0.354167
+24 0.689063 0.493750 0.134375 0.191667
+24 0.584375 0.777083 0.181250 0.254167
\ No newline at end of file
diff --git a/dataset/labels/val/val_0015.txt b/dataset/labels/val/val_0015.txt
new file mode 100644
index 0000000..2ba7a69
--- /dev/null
+++ b/dataset/labels/val/val_0015.txt
@@ -0,0 +1,2 @@
+12 0.535937 0.622917 0.196875 0.179167
+9 0.472656 0.180208 0.145313 0.231250
\ No newline at end of file
diff --git a/dataset/labels/val/val_0016.txt b/dataset/labels/val/val_0016.txt
new file mode 100644
index 0000000..d6a4015
--- /dev/null
+++ b/dataset/labels/val/val_0016.txt
@@ -0,0 +1 @@
+23 0.835938 0.352083 0.193750 0.233333
\ No newline at end of file
diff --git a/dataset/labels/val/val_0017.txt b/dataset/labels/val/val_0017.txt
new file mode 100644
index 0000000..88a528d
--- /dev/null
+++ b/dataset/labels/val/val_0017.txt
@@ -0,0 +1,3 @@
+19 0.198437 0.806250 0.137500 0.220833
+20 0.161719 0.572917 0.207813 0.237500
+10 0.692969 0.588542 0.104688 0.147917
\ No newline at end of file
diff --git a/dataset/labels/val/val_0018.txt b/dataset/labels/val/val_0018.txt
new file mode 100644
index 0000000..cf19af6
--- /dev/null
+++ b/dataset/labels/val/val_0018.txt
@@ -0,0 +1,2 @@
+0 0.447656 0.572917 0.204687 0.308333
+0 0.218750 0.634375 0.200000 0.222917
\ No newline at end of file
diff --git a/dataset/labels/val/val_0019.txt b/dataset/labels/val/val_0019.txt
new file mode 100644
index 0000000..d1358e2
--- /dev/null
+++ b/dataset/labels/val/val_0019.txt
@@ -0,0 +1 @@
+26 0.218750 0.212500 0.140625 0.237500
\ No newline at end of file
diff --git a/dataset/labels/val/val_0020.txt b/dataset/labels/val/val_0020.txt
new file mode 100644
index 0000000..708ffe4
--- /dev/null
+++ b/dataset/labels/val/val_0020.txt
@@ -0,0 +1 @@
+12 0.308594 0.383333 0.185938 0.170833
\ No newline at end of file
diff --git a/dataset/labels/val/val_0021.txt b/dataset/labels/val/val_0021.txt
new file mode 100644
index 0000000..7fae6bd
--- /dev/null
+++ b/dataset/labels/val/val_0021.txt
@@ -0,0 +1,2 @@
+27 0.161719 0.785417 0.182812 0.266667
+5 0.425781 0.789583 0.173437 0.183333
\ No newline at end of file
diff --git a/dataset/labels/val/val_0022.txt b/dataset/labels/val/val_0022.txt
new file mode 100644
index 0000000..fa06cbb
--- /dev/null
+++ b/dataset/labels/val/val_0022.txt
@@ -0,0 +1,3 @@
+11 0.608594 0.715625 0.160938 0.306250
+7 0.554688 0.216667 0.171875 0.195833
+4 0.860156 0.587500 0.173437 0.108333
\ No newline at end of file
diff --git a/dataset/labels/val/val_0023.txt b/dataset/labels/val/val_0023.txt
new file mode 100644
index 0000000..ce72ad8
--- /dev/null
+++ b/dataset/labels/val/val_0023.txt
@@ -0,0 +1,2 @@
+22 0.634375 0.735417 0.134375 0.179167
+23 0.457031 0.771875 0.154688 0.202083
\ No newline at end of file
diff --git a/dataset/labels/val/val_0024.txt b/dataset/labels/val/val_0024.txt
new file mode 100644
index 0000000..27df534
--- /dev/null
+++ b/dataset/labels/val/val_0024.txt
@@ -0,0 +1,2 @@
+2 0.221875 0.685417 0.184375 0.287500
+8 0.599219 0.251042 0.176563 0.135417
\ No newline at end of file
diff --git a/dataset/labels/val/val_0025.txt b/dataset/labels/val/val_0025.txt
new file mode 100644
index 0000000..787c496
--- /dev/null
+++ b/dataset/labels/val/val_0025.txt
@@ -0,0 +1 @@
+9 0.250781 0.379167 0.160938 0.175000
\ No newline at end of file
diff --git a/dataset/labels/val/val_0026.txt b/dataset/labels/val/val_0026.txt
new file mode 100644
index 0000000..40aefa1
--- /dev/null
+++ b/dataset/labels/val/val_0026.txt
@@ -0,0 +1,2 @@
+7 0.128125 0.308333 0.146875 0.120833
+14 0.268750 0.432292 0.156250 0.210417
\ No newline at end of file
diff --git a/dataset/labels/val/val_0027.txt b/dataset/labels/val/val_0027.txt
new file mode 100644
index 0000000..85c0302
--- /dev/null
+++ b/dataset/labels/val/val_0027.txt
@@ -0,0 +1,3 @@
+26 0.715625 0.487500 0.071875 0.216667
+23 0.469531 0.445833 0.223438 0.266667
+25 0.677344 0.803125 0.135937 0.164583
\ No newline at end of file
diff --git a/dataset/labels/val/val_0028.txt b/dataset/labels/val/val_0028.txt
new file mode 100644
index 0000000..4704a15
--- /dev/null
+++ b/dataset/labels/val/val_0028.txt
@@ -0,0 +1,2 @@
+13 0.789062 0.184375 0.181250 0.206250
+10 0.666406 0.541667 0.117188 0.145833
\ No newline at end of file
diff --git a/dataset/labels/val/val_0029.txt b/dataset/labels/val/val_0029.txt
new file mode 100644
index 0000000..7e792b3
--- /dev/null
+++ b/dataset/labels/val/val_0029.txt
@@ -0,0 +1,2 @@
+19 0.864844 0.432292 0.164062 0.264583
+1 0.557813 0.423958 0.209375 0.281250
\ No newline at end of file
diff --git a/dataset/labels/val/val_0030.txt b/dataset/labels/val/val_0030.txt
new file mode 100644
index 0000000..3fe08da
--- /dev/null
+++ b/dataset/labels/val/val_0030.txt
@@ -0,0 +1,3 @@
+1 0.534375 0.515625 0.184375 0.268750
+20 0.596875 0.223958 0.209375 0.247917
+1 0.336719 0.323958 0.189062 0.293750
\ No newline at end of file
diff --git a/dataset/labels/val/val_0031.txt b/dataset/labels/val/val_0031.txt
new file mode 100644
index 0000000..ce2ed65
--- /dev/null
+++ b/dataset/labels/val/val_0031.txt
@@ -0,0 +1,3 @@
+9 0.185156 0.766667 0.189062 0.183333
+12 0.418750 0.559375 0.125000 0.185417
+22 0.333594 0.702083 0.157812 0.208333
\ No newline at end of file
diff --git a/dataset/labels/val/val_0032.txt b/dataset/labels/val/val_0032.txt
new file mode 100644
index 0000000..0bdfadb
--- /dev/null
+++ b/dataset/labels/val/val_0032.txt
@@ -0,0 +1 @@
+27 0.840625 0.347917 0.134375 0.183333
\ No newline at end of file
diff --git a/dataset/labels/val/val_0033.txt b/dataset/labels/val/val_0033.txt
new file mode 100644
index 0000000..fef7dff
--- /dev/null
+++ b/dataset/labels/val/val_0033.txt
@@ -0,0 +1,3 @@
+9 0.255469 0.641667 0.160938 0.270833
+25 0.172656 0.473958 0.182812 0.252083
+25 0.729688 0.327083 0.193750 0.283333
\ No newline at end of file
diff --git a/dataset/labels/val/val_0034.txt b/dataset/labels/val/val_0034.txt
new file mode 100644
index 0000000..98d4e29
--- /dev/null
+++ b/dataset/labels/val/val_0034.txt
@@ -0,0 +1 @@
+16 0.859375 0.439583 0.178125 0.258333
\ No newline at end of file
diff --git a/dataset/labels/val/val_0035.txt b/dataset/labels/val/val_0035.txt
new file mode 100644
index 0000000..4c1ee45
--- /dev/null
+++ b/dataset/labels/val/val_0035.txt
@@ -0,0 +1,3 @@
+26 0.578125 0.492708 0.087500 0.310417
+15 0.832031 0.735417 0.089063 0.195833
+24 0.207813 0.308333 0.140625 0.187500
\ No newline at end of file
diff --git a/dataset/labels/val/val_0036.txt b/dataset/labels/val/val_0036.txt
new file mode 100644
index 0000000..d0a9b47
--- /dev/null
+++ b/dataset/labels/val/val_0036.txt
@@ -0,0 +1 @@
+16 0.412500 0.809375 0.134375 0.189583
\ No newline at end of file
diff --git a/dataset/labels/val/val_0037.txt b/dataset/labels/val/val_0037.txt
new file mode 100644
index 0000000..d1a91d8
--- /dev/null
+++ b/dataset/labels/val/val_0037.txt
@@ -0,0 +1 @@
+25 0.157031 0.555208 0.201563 0.243750
\ No newline at end of file
diff --git a/dataset/labels/val/val_0038.txt b/dataset/labels/val/val_0038.txt
new file mode 100644
index 0000000..8269e45
--- /dev/null
+++ b/dataset/labels/val/val_0038.txt
@@ -0,0 +1,2 @@
+9 0.705469 0.239583 0.173437 0.316667
+27 0.752344 0.507292 0.151562 0.185417
\ No newline at end of file
diff --git a/dataset/labels/val/val_0039.txt b/dataset/labels/val/val_0039.txt
new file mode 100644
index 0000000..daa95cd
--- /dev/null
+++ b/dataset/labels/val/val_0039.txt
@@ -0,0 +1,2 @@
+0 0.188281 0.322917 0.160938 0.200000
+13 0.389062 0.382292 0.190625 0.235417
\ No newline at end of file
diff --git a/dataset/labels/val/val_0040.txt b/dataset/labels/val/val_0040.txt
new file mode 100644
index 0000000..49462af
--- /dev/null
+++ b/dataset/labels/val/val_0040.txt
@@ -0,0 +1,2 @@
+14 0.744531 0.808333 0.167187 0.204167
+9 0.612500 0.440625 0.134375 0.214583
\ No newline at end of file
diff --git a/dataset/labels/val/val_0041.txt b/dataset/labels/val/val_0041.txt
new file mode 100644
index 0000000..aef8085
--- /dev/null
+++ b/dataset/labels/val/val_0041.txt
@@ -0,0 +1,2 @@
+1 0.375000 0.615625 0.212500 0.272917
+5 0.764844 0.344792 0.226562 0.210417
\ No newline at end of file
diff --git a/dataset/labels/val/val_0042.txt b/dataset/labels/val/val_0042.txt
new file mode 100644
index 0000000..c13277b
--- /dev/null
+++ b/dataset/labels/val/val_0042.txt
@@ -0,0 +1,3 @@
+8 0.596875 0.235417 0.118750 0.154167
+17 0.658594 0.617708 0.085938 0.343750
+8 0.124219 0.580208 0.132812 0.118750
\ No newline at end of file
diff --git a/dataset/labels/val/val_0043.txt b/dataset/labels/val/val_0043.txt
new file mode 100644
index 0000000..17e0e26
--- /dev/null
+++ b/dataset/labels/val/val_0043.txt
@@ -0,0 +1,3 @@
+11 0.263281 0.582292 0.145313 0.168750
+6 0.761719 0.626042 0.182812 0.243750
+20 0.142969 0.318750 0.167187 0.245833
\ No newline at end of file
diff --git a/dataset/labels/val/val_0044.txt b/dataset/labels/val/val_0044.txt
new file mode 100644
index 0000000..ee8037d
--- /dev/null
+++ b/dataset/labels/val/val_0044.txt
@@ -0,0 +1,3 @@
+10 0.261719 0.383333 0.073438 0.108333
+27 0.620313 0.764583 0.162500 0.208333
+20 0.314063 0.747917 0.181250 0.237500
\ No newline at end of file
diff --git a/dataset/labels/val/val_0045.txt b/dataset/labels/val/val_0045.txt
new file mode 100644
index 0000000..8456a4a
--- /dev/null
+++ b/dataset/labels/val/val_0045.txt
@@ -0,0 +1,3 @@
+19 0.684375 0.617708 0.165625 0.247917
+5 0.323437 0.287500 0.153125 0.291667
+0 0.803125 0.491667 0.200000 0.320833
\ No newline at end of file
diff --git a/dataset/labels/val/val_0046.txt b/dataset/labels/val/val_0046.txt
new file mode 100644
index 0000000..ad991a7
--- /dev/null
+++ b/dataset/labels/val/val_0046.txt
@@ -0,0 +1 @@
+23 0.582812 0.330208 0.134375 0.177083
\ No newline at end of file
diff --git a/dataset/labels/val/val_0047.txt b/dataset/labels/val/val_0047.txt
new file mode 100644
index 0000000..849c782
--- /dev/null
+++ b/dataset/labels/val/val_0047.txt
@@ -0,0 +1,3 @@
+7 0.433594 0.615625 0.139063 0.118750
+17 0.402344 0.881250 0.239063 0.100000
+8 0.108594 0.761458 0.089063 0.202083
\ No newline at end of file
diff --git a/dataset/labels/val/val_0048.txt b/dataset/labels/val/val_0048.txt
new file mode 100644
index 0000000..c82e9c3
--- /dev/null
+++ b/dataset/labels/val/val_0048.txt
@@ -0,0 +1,3 @@
+27 0.672656 0.398958 0.135937 0.164583
+4 0.532031 0.392708 0.182812 0.131250
+18 0.342187 0.693750 0.153125 0.200000
\ No newline at end of file
diff --git a/dataset/labels/val/val_0049.txt b/dataset/labels/val/val_0049.txt
new file mode 100644
index 0000000..bc55fc7
--- /dev/null
+++ b/dataset/labels/val/val_0049.txt
@@ -0,0 +1,2 @@
+11 0.203125 0.679167 0.143750 0.233333
+13 0.606250 0.780208 0.146875 0.227083
\ No newline at end of file
diff --git a/dummy.onnx b/dummy.onnx
new file mode 100644
index 0000000..7d3ac49
Binary files /dev/null and b/dummy.onnx differ
diff --git a/dummy2.onnx b/dummy2.onnx
new file mode 100644
index 0000000..4a2e075
Binary files /dev/null and b/dummy2.onnx differ
diff --git a/export.py b/export.py
new file mode 100644
index 0000000..1cb84c4
--- /dev/null
+++ b/export.py
@@ -0,0 +1,114 @@
+import os
+import time
+import argparse
+import torch
+import torchvision
+import onnx
+import onnxruntime as ort
+import numpy as np
+
+def export_to_onnx(model_path, onnx_path):
+    print(f"Loading PyTorch checkpoint from {model_path}...")
+    from data_generator import CLASSES
+    num_classes = len(CLASSES) + 1
+    
+    # Recreate the model structure
+    model = torchvision.models.detection.ssdlite320_mobilenet_v3_large(
+        num_classes=num_classes,
+        weights_backbone=torchvision.models.MobileNet_V3_Large_Weights.DEFAULT
+    )
+    
+    # Load state dict
+    state_dict = torch.load(model_path, map_location='cpu')
+    model.load_state_dict(state_dict)
+    model.eval()
+    
+    # Dummy input: list of 1 tensor of size [3, 320, 320]
+    # SSDLite in torchvision expects a list of tensors during export
+    dummy_input = [torch.randn(3, 320, 320)]
+    
+    print("Exporting model to ONNX...")
+    os.makedirs(os.path.dirname(onnx_path), exist_ok=True)
+    
+    torch.onnx.export(
+        model,
+        (dummy_input,),
+        onnx_path,
+        opset_version=12,
+        input_names=["images"],
+        output_names=["boxes", "scores", "labels"],
+        dynamic_axes={
+            "images": {0: "batch_size"}
+        }
+    )
+    
+    print(f"Successfully saved ONNX model to {onnx_path}")
+    
+    # Verify ONNX model
+    print("Verifying ONNX model graph structure...")
+    onnx_model = onnx.load(onnx_path)
+    onnx.checker.check_model(onnx_model)
+    
+    print("Inputs:")
+    for input_node in onnx_model.graph.input:
+        print(f"  - Name: {input_node.name}")
+    print("Outputs:")
+    for output_node in onnx_model.graph.output:
+        print(f"  - Name: {output_node.name}")
+
+def benchmark_onnx(onnx_path):
+    print(f"Benchmarking ONNX inference speed using ONNX Runtime...")
+    
+    # Create inference session
+    providers = ['CPUExecutionProvider']
+    if 'DirectMLExecutionProvider' in ort.get_available_providers():
+        providers = ['DirectMLExecutionProvider', 'CPUExecutionProvider']
+        print("Using DirectML hardware acceleration!")
+        
+    session = ort.InferenceSession(onnx_path, providers=providers)
+    
+    # Prepare dummy input matching the shape of the exported model input
+    # Note: torchvision SSD ONNX export expects list of images.
+    # The input node shape in ONNX has dynamic axes. Let's create a float32 array
+    # corresponding to the image. SSDLite internally resizes and batches.
+    # Let's see the input signature. The input 'images' expects a 3D or 4D float tensor.
+    # In our export script, the dummy input was [torch.randn(3, 320, 320)]
+    # In ONNX, a list of Tensors gets mapped to a single concatenated batch tensor or similar.
+    # Let's generate a random array of shape (1, 3, 320, 320)
+    input_name = session.get_inputs()[0].name
+    dummy_in = np.random.randn(3, 320, 320).astype(np.float32)
+    
+    # Warmup
+    for _ in range(5):
+        _ = session.run(None, {input_name: dummy_in})
+        
+    # Measure latency
+    num_runs = 50
+    start_time = time.time()
+    for _ in range(num_runs):
+        _ = session.run(None, {input_name: dummy_in})
+    elapsed = time.time() - start_time
+    avg_latency_ms = (elapsed / num_runs) * 1000
+    
+    print(f"Average ONNX Runtime inference latency: {avg_latency_ms:.2f} ms")
+    if avg_latency_ms < 50.0:
+        print("Success: Inference latency is under the 50 ms limit!")
+    else:
+        print("Warning: Inference latency is above the 50 ms limit. Optimize hardware providers.")
+        
+    return avg_latency_ms
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Export PyTorch SSDLite model to ONNX")
+    parser.add_argument("--pytorch-model", type=str, default=os.path.join("models", "model.pt"), help="Path to PyTorch model weights")
+    parser.add_argument("--onnx-model", type=str, default=os.path.join("models", "model.onnx"), help="Path to save ONNX model")
+    
+    args = parser.parse_args()
+    
+    try:
+        export_to_onnx(args.pytorch_model, args.onnx_model)
+        benchmark_onnx(args.onnx_model)
+    except Exception as e:
+        print(f"Error during export: {e}")
+        import sys
+        sys.exit(1)
diff --git a/inference.py b/inference.py
new file mode 100644
index 0000000..33d65ea
--- /dev/null
+++ b/inference.py
@@ -0,0 +1,131 @@
+import os
+import cv2
+import numpy as np
+import onnxruntime as ort
+
+from data_generator import CLASSES
+
+class FruitVegetableDetector:
+    def __init__(self, model_path=os.path.join("models", "model.onnx")):
+        self.model_path = model_path
+        self.session = None
+        self.input_name = None
+        self.load_model()
+        
+    def load_model(self):
+        if not os.path.exists(self.model_path):
+            print(f"Model file {self.model_path} not found. Running in SIMULATED mode.")
+            self.session = None
+            return
+            
+        try:
+            # Set providers. SSDLite ONNX Runtime runs extremely fast on CPU
+            providers = ['CPUExecutionProvider']
+            if 'DirectMLExecutionProvider' in ort.get_available_providers():
+                providers = ['DirectMLExecutionProvider', 'CPUExecutionProvider']
+                
+            self.session = ort.InferenceSession(self.model_path, providers=providers)
+            self.input_name = self.session.get_inputs()[0].name
+            print(f"ONNX model loaded successfully from {self.model_path}")
+        except Exception as e:
+            print(f"Failed to load ONNX model: {e}. Falling back to SIMULATED mode.")
+            self.session = None
+
+    def detect(self, image_rgb, confidence_threshold=0.10):
+        """
+        Runs inference on an RGB image.
+        Args:
+            image_rgb (np.ndarray): HWC RGB image.
+            confidence_threshold (float): Minimum confidence to report.
+        Returns:
+            list: List of predictions with class, confidence, and box [xmin, ymin, xmax, ymax]
+        """
+        orig_h, orig_w, _ = image_rgb.shape
+        
+        # If no model is loaded, run simulation fallback
+        if self.session is None:
+            return self._simulate_detection(orig_w, orig_h, confidence_threshold)
+            
+        # Preprocessing: Resize to 320x320
+        resized = cv2.resize(image_rgb, (320, 320))
+        # Scale to [0.0, 1.0] and transpose to [C, H, W]
+        input_tensor = resized.astype(np.float32) / 255.0
+        input_tensor = np.transpose(input_tensor, (2, 0, 1)) # shape: (3, 320, 320)
+        
+        # Run inference
+        outputs = self.session.run(None, {self.input_name: input_tensor})
+        boxes, scores, labels = outputs
+        
+        predictions = []
+        for i in range(len(scores)):
+            score = float(scores[i])
+            if score < confidence_threshold:
+                continue
+                
+            # Class ID is 1-indexed, convert to 0-indexed
+            class_id = int(labels[i]) - 1
+            if class_id < 0 or class_id >= len(CLASSES):
+                continue
+                
+            class_name = CLASSES[class_id]
+            box_320 = boxes[i]
+            
+            # Scale box back to original coordinates
+            xmin = int(round((box_320[0] / 320.0) * orig_w))
+            ymin = int(round((box_320[1] / 320.0) * orig_h))
+            xmax = int(round((box_320[2] / 320.0) * orig_w))
+            ymax = int(round((box_320[3] / 320.0) * orig_h))
+            
+            # Clip to image boundaries
+            xmin = max(0, min(xmin, orig_w - 1))
+            ymin = max(0, min(ymin, orig_h - 1))
+            xmax = max(xmin + 1, min(xmax, orig_w))
+            ymax = max(ymin + 1, min(ymax, orig_h))
+            
+            predictions.append({
+                "class": class_name,
+                "confidence": round(score, 3),
+                "box": [xmin, ymin, xmax, ymax]
+            })
+            
+        # Sort predictions by confidence descending
+        predictions = sorted(predictions, key=lambda x: x["confidence"], reverse=True)
+        return predictions
+
+    def _simulate_detection(self, width, height, confidence_threshold):
+        """
+        Generates simulated detections based on random chances.
+        Used as a fallback when the model hasn't been trained yet.
+        """
+        # Seed a pseudo-random detection based on image size to look semi-stable
+        seed_factor = (width + height) % 6
+        
+        # Simulate 1 to 2 detections
+        predictions = []
+        
+        # 1. First main prediction (high confidence)
+        class_idx = seed_factor
+        conf = 0.85 + (seed_factor * 0.02)
+        
+        # Simulate reasonable bounding box centered on scale
+        cx, cy = int(width * 0.5), int(height * 0.5)
+        w, h = int(width * 0.35), int(height * 0.45)
+        
+        predictions.append({
+            "class": CLASSES[class_idx],
+            "confidence": round(conf, 3),
+            "box": [cx - w//2, cy - h//2, cx + w//2, cy + h//2]
+        })
+        
+        # 2. Second prediction (low confidence, e.g. alternative suggestion)
+        alt_class_idx = (class_idx + 1) % len(CLASSES)
+        alt_conf = 0.12 + (seed_factor * 0.01)
+        
+        if alt_conf >= confidence_threshold:
+            predictions.append({
+                "class": CLASSES[alt_class_idx],
+                "confidence": round(alt_conf, 3),
+                "box": [cx - w//2 + 20, cy - h//2 + 10, cx + w//2 + 20, cy + h//2 + 10]
+            })
+            
+        return predictions
diff --git a/models/model.onnx b/models/model.onnx
new file mode 100644
index 0000000..ef63f8d
Binary files /dev/null and b/models/model.onnx differ
diff --git a/models/model.pt b/models/model.pt
new file mode 100644
index 0000000..d341626
Binary files /dev/null and b/models/model.pt differ
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..467468c
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,3 @@
+fastapi
+uvicorn
+albumentations
diff --git a/train.py b/train.py
new file mode 100644
index 0000000..8bb1273
--- /dev/null
+++ b/train.py
@@ -0,0 +1,199 @@
+import os
+import sys
+import json
+import time
+import argparse
+import torch
+import torchvision
+from torch.utils.data import DataLoader
+import albumentations as A
+import numpy as np
+
+from dataset import FruitVegetableDataset, collate_fn
+from data_generator import build_dataset
+
+STATUS_FILE = "training_status.json"
+
+def update_status(status, epoch=0, total_epochs=0, train_loss=[], val_loss=[], progress=0.0, message=""):
+    """
+    Saves current training status to a JSON file so that the FastAPI server
+    can read it and report it to the frontend UI in real-time.
+    """
+    status_data = {
+        "status": status,
+        "epoch": epoch,
+        "total_epochs": total_epochs,
+        "train_loss": train_loss,
+        "val_loss": val_loss,
+        "progress": progress,
+        "message": message,
+        "timestamp": time.time()
+    }
+    with open(STATUS_FILE, "w") as f:
+        json.dump(status_data, f, indent=2)
+
+def train(args):
+    # Ensure dataset exists
+    if not os.path.exists(args.dataset_dir) or len(os.listdir(os.path.join(args.dataset_dir, "images", "train"))) == 0:
+        update_status("generating_data", message="Generating synthetic fruit & vegetable dataset...")
+        print("Generating dataset...")
+        build_dataset(base_dir=args.dataset_dir, train_count=200, val_count=50)
+        
+    update_status("training", epoch=0, total_epochs=args.epochs, progress=5.0, message="Initializing model and loading dataset...")
+    
+    # Define augmentations using Albumentations
+    train_transform = A.Compose([
+        A.Resize(320, 320),
+        A.HorizontalFlip(p=0.5),
+        A.ShiftScaleRotate(shift_limit=0.1, scale_limit=0.15, rotate_limit=30, p=0.7),
+        A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.7),
+        A.GaussNoise(p=0.3),
+    ], bbox_params=A.BboxParams(format='pascal_voc', label_fields=['category_ids']))
+    
+    val_transform = A.Compose([
+        A.Resize(320, 320),
+    ], bbox_params=A.BboxParams(format='pascal_voc', label_fields=['category_ids']))
+    
+    # Datasets and loaders
+    train_dataset = FruitVegetableDataset(
+        images_dir=os.path.join(args.dataset_dir, "images", "train"),
+        labels_dir=os.path.join(args.dataset_dir, "labels", "train"),
+        transform=train_transform
+    )
+    
+    val_dataset = FruitVegetableDataset(
+        images_dir=os.path.join(args.dataset_dir, "images", "val"),
+        labels_dir=os.path.join(args.dataset_dir, "labels", "val"),
+        transform=val_transform
+    )
+    
+    train_loader = DataLoader(
+        train_dataset,
+        batch_size=args.batch_size,
+        shuffle=True,
+        num_workers=0, # 0 works best on Windows to avoid multi-processing issues
+        collate_fn=collate_fn,
+        drop_last=True
+    )
+    
+    val_loader = DataLoader(
+        val_dataset,
+        batch_size=args.batch_size,
+        shuffle=False,
+        num_workers=0,
+        collate_fn=collate_fn
+    )
+    
+    # Set number of classes dynamically (classes + 1 background)
+    from data_generator import CLASSES
+    num_classes = len(CLASSES) + 1
+    
+    print("Building SSDLite MobileNetV3 model...")
+    # Load pretrained model with backbone weights
+    model = torchvision.models.detection.ssdlite320_mobilenet_v3_large(
+        num_classes=num_classes,
+        weights_backbone=torchvision.models.MobileNet_V3_Large_Weights.DEFAULT
+    )
+    
+    # Send to GPU if available, else CPU
+    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+    model.to(device)
+    
+    # Optimizer and learning rate scheduler
+    params = [p for p in model.parameters() if p.requires_grad]
+    optimizer = torch.optim.AdamW(params, lr=args.lr, weight_decay=0.0005)
+    lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs)
+    
+    train_loss_history = []
+    val_loss_history = []
+    
+    print(f"Starting training on {device} for {args.epochs} epochs...")
+    for epoch in range(1, args.epochs + 1):
+        # 1. Training Phase
+        model.train()
+        epoch_train_losses = []
+        
+        for batch_idx, (images, targets) in enumerate(train_loader):
+            images = [img.to(device) for img in images]
+            targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
+            
+            # Forward pass: model returns a dict of losses in training mode
+            loss_dict = model(images, targets)
+            losses = sum(loss for loss in loss_dict.values())
+            
+            optimizer.zero_grad()
+            losses.backward()
+            optimizer.step()
+            
+            epoch_train_losses.append(losses.item())
+            
+        train_loss = np.mean(epoch_train_losses)
+        train_loss_history.append(train_loss)
+        
+        # 2. Validation Phase (compute validation loss by running model in train mode with no grad)
+        model.train() # Must remain in train mode to output losses
+        epoch_val_losses = []
+        with torch.no_grad():
+            for images, targets in val_loader:
+                images = [img.to(device) for img in images]
+                targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
+                
+                loss_dict = model(images, targets)
+                losses = sum(loss for loss in loss_dict.values())
+                epoch_val_losses.append(losses.item())
+                
+        val_loss = np.mean(epoch_val_losses)
+        val_loss_history.append(val_loss)
+        
+        # Step the scheduler
+        lr_scheduler.step()
+        
+        # Update progress reporting
+        progress = 10.0 + (epoch / args.epochs) * 80.0 # scale 10% to 90%
+        msg = f"Epoch {epoch}/{args.epochs} - Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}"
+        print(msg)
+        update_status(
+            status="training",
+            epoch=epoch,
+            total_epochs=args.epochs,
+            train_loss=train_loss_history,
+            val_loss=val_loss_history,
+            progress=progress,
+            message=msg
+        )
+        
+    # Save the PyTorch weights
+    os.makedirs("models", exist_ok=True)
+    pytorch_model_path = os.path.join("models", "model.pt")
+    torch.save(model.state_dict(), pytorch_model_path)
+    
+    msg = f"Training completed successfully! Saved PyTorch model to {pytorch_model_path}"
+    print(msg)
+    update_status(
+        status="completed",
+        epoch=args.epochs,
+        total_epochs=args.epochs,
+        train_loss=train_loss_history,
+        val_loss=val_loss_history,
+        progress=100.0,
+        message=msg
+    )
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Train SSDLite MobileNetV3 on Fruit & Vegetable Dataset")
+    parser.add_argument("--dataset-dir", type=str, default="dataset", help="Path to dataset directory")
+    parser.add_argument("--epochs", type=int, default=15, help="Number of training epochs")
+    parser.add_argument("--batch-size", type=int, default=8, help="Batch size for training")
+    parser.add_argument("--lr", type=float, default=0.001, help="Learning rate")
+    
+    args = parser.parse_args()
+    
+    try:
+        # Initial status write
+        update_status("idle", message="Starting training script...")
+        train(args)
+    except Exception as e:
+        err_msg = f"Error during training: {str(e)}"
+        print(err_msg, file=sys.stderr)
+        update_status("failed", message=err_msg)
+        sys.exit(1)
diff --git a/training_status.json b/training_status.json
new file mode 100644
index 0000000..b817b2a
--- /dev/null
+++ b/training_status.json
@@ -0,0 +1,172 @@
+{
+  "status": "completed",
+  "epoch": 80,
+  "total_epochs": 80,
+  "train_loss": [
+    14.416558946881976,
+    8.560535499027797,
+    6.1590442316872736,
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+    0.786924170596259,
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+    0.7780132591724396,
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+    0.8371244583811078,
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+    0.7904618041855949,
+    0.7480435158525195,
+    0.8140954801014492,
+    0.7576784746987479,
+    0.8081949864115033,
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+    0.7453874221869877,
+    0.788932808807918,
+    0.7627504255090441,
+    0.7890771670000893
+  ],
+  "val_loss": [
+    11.423399448394775,
+    7.716830611228943,
+    6.559785723686218,
+    5.9326852560043335,
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+    3.5334702134132385,
+    3.5319539308547974
+  ],
+  "progress": 100.0,
+  "message": "Training completed successfully! Saved PyTorch model to models\\model.pt",
+  "timestamp": 1780007552.618948
+}
\ No newline at end of file