Initial commit of web application

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# Virtual Environment
+venv/
+
+# Python cache
+__pycache__/
+*.pyc
+
+# macOS system file
+.DS_Store
+
+# Datasets (should not be in Git)
+datasets/
+
+# YOLOv8 runs and trained models (these are outputs)
+runs/
+# best.pt
+test1.mp4
+test2.mp4
+
+# Temporary uploads folder
+uploads/

Dockerfile

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+# Start from a standard, public Python 3.9 image
+FROM python:3.9-slim
+
+# Install system dependencies required by OpenCV
+RUN apt-get update && apt-get install -y libgl1-mesa-glx libglib2.0-0
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements file and install dependencies
+COPY requirements.txt ./
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of your application code into the container
+COPY . .
+
+# Expose the port that the app will run on
+EXPOSE 7860
+
+# Command to run your FastAPI application using Uvicorn
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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+---
+title: Workspace Safety Detection
+emoji: 🛡️
+colorFrom: indigo
+colorTo: gray
+sdk: docker
+app_file: app.py
+---
+
+# Workplace Safety PPE Detection using YOLOv8
+
+This project provides a real-time object detection system to monitor whether people in a workplace environment are wearing essential Personal Protective Equipment (PPE), specifically safety helmets and vests. The system is built using the YOLOv8 model.
+
+---
+
+## Key Features
+
+* **Real-Time Detection:** Capable of processing video streams from files or a live webcam to identify PPE in real-time.
+* **High Accuracy:** Trained on a large dataset of over 23,000 images, achieving an mAP50 of 73.5% overall and over 86% for key classes like helmets and vests.
+* **State-of-the-Art Model:** Utilizes YOLOv8, a powerful and efficient object detection architecture.
+* **Focused Detection:** While trained on 17 classes, the inference script is configured to specifically highlight helmets and vests for workplace safety monitoring.
+
+---
+
+## Technology Stack
+
+* **Python 3.8+**
+* **PyTorch**
+* **Ultralytics YOLOv8**
+* **OpenCV**
+* **Kaggle Notebooks** (for training)
+
+---
+
+## Getting Started
+
+Follow these instructions to set up and run the project on your local machine.
+
+### Prerequisites
+
+* Python 3.8 or newer
+* Git
+
+### Installation
+
+1.  **Clone the repository:**
+    ```bash
+    git clone [https://github.com/hafizqaim/Workspace-Safety-Detection-using-YOLOv8.git](https://github.com/hafizqaim/Workspace-Safety-Detection-using-YOLOv8.git)
+    cd Workspace-Safety-Detection-using-YOLOv8
+    ```
+
+2.  **Create and activate a virtual environment:**
+    ```bash
+    # For macOS/Linux
+    python3 -m venv venv
+    source venv/bin/activate
+
+    # For Windows
+    python -m venv venv
+    .\venv\Scripts\activate
+    ```
+
+3.  **Install the required packages:**
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+---
+
+## Usage
+
+1.  **Download the Trained Model:**
+    The trained model file (`best.pt`) is required to run the inference. Download it from the **[Releases](https://github.com/hafizqaim/Workspace-Safety-Detection-using-YOLOv8/releases)** page of this repository.
+
+2.  **Place the Model:**
+    Place the downloaded `best.pt` file in the root directory of the project.
+
+3.  **Run the Inference Script:**
+    The `inference.py` script is configured to run on your webcam by default.
+    ```bash
+    python inference.py
+    ```
+    * To use a video file instead, open `inference.py` and modify the script to point to your video file.
+
+---
+
+## Model Performance
+
+The model was trained for 10 epochs on the "PPE Detection v3" dataset from Roboflow.
+
+| Class               | Precision | Recall | mAP50 | mAP50-95 |
+| :------------------ | :-------- | :----- | :---- | :------- |
+| **Overall** | 0.72      | 0.715  | 0.735 | 0.456    |
+| **`head_helmet`** | 0.784     | 0.824  | 0.866 | 0.584    |
+| **`vest`** | 0.841     | 0.897  | 0.935 | 0.705    |
+
+<img width="1440" height="810" alt="Screenshot 2025-07-16 at 11 53 44 AM" src="https://github.com/user-attachments/assets/d01fadda-a590-4525-acbc-7006c007cd19" />
+<img width="1314" height="771" alt="Screenshot 2025-07-16 at 11 55 09 AM" src="https://github.com/user-attachments/assets/0fe27deb-48b2-4dae-bfc2-6fec0673bfa1" />

app.py

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+import cv2
+import uuid
+import os
+import shutil
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import HTMLResponse, JSONResponse, StreamingResponse
+from ultralytics import YOLO
+
+# --- Initialization ---
+app = FastAPI()
+
+# Load the pre-trained YOLOv8 model
+# Ensure 'best.pt' is in the same directory as this script
+try:
+    model = YOLO('best.pt')
+except Exception as e:
+    print(f"Error loading model: {e}")
+    # Exit if the model can't be loaded, as the app is useless without it.
+    exit()
+
+# A dictionary to store the paths of uploaded videos, using a session ID as the key
+video_sessions = {}
+
+# Create a directory to store temporary video uploads
+UPLOAD_DIR = "uploads"
+os.makedirs(UPLOAD_DIR, exist_ok=True)
+
+
+# --- Frontend Endpoint ---
+
+@app.get("/", response_class=HTMLResponse)
+async def read_root():
+    """Serves the main HTML page."""
+    try:
+        with open("templates/index.html") as f:
+            return f.read()
+    except FileNotFoundError:
+        return "<h1>Error: index.html not found</h1><p>Please make sure the 'templates' folder and 'index.html' file exist.</p>"
+
+
+# --- Webcam Streaming ---
+
+def generate_webcam_frames():
+    """Generates annotated frames from the webcam."""
+    camera = cv2.VideoCapture(0)  # 0 is the default webcam
+    if not camera.isOpened():
+        print("Error: Could not start webcam.")
+        return
+
+    while True:
+        success, frame = camera.read()
+        if not success:
+            break
+        else:
+            # Run YOLOv8 inference on the frame, filtering for helmets (12) and vests (16)
+            results = model(frame, classes=[12, 16], verbose=False)
+            annotated_frame = results[0].plot()
+
+            # Encode the frame as JPEG
+            ret, buffer = cv2.imencode('.jpg', annotated_frame)
+            if not ret:
+                continue
+            frame_bytes = buffer.tobytes()
+
+            # Yield the frame in the multipart format
+            yield (b'--frame\r\n'
+                   b'Content-Type: image/jpeg\r\n\r\n' + frame_bytes + b'\r\n')
+    
+    camera.release()
+
+@app.get("/video_feed/webcam")
+def video_feed_webcam():
+    """Streams video from the webcam."""
+    # Use StreamingResponse for generator functions
+    return StreamingResponse(generate_webcam_frames(), media_type="multipart/x-mixed-replace; boundary=frame")
+
+
+# --- Video Upload and Processing ---
+
+@app.post("/upload_video")
+async def upload_video(video_file: UploadFile = File(...)):
+    """Handles video file uploads."""
+    session_id = str(uuid.uuid4())
+    file_path = os.path.join(UPLOAD_DIR, f"{session_id}_{video_file.filename}")
+
+    # Save the uploaded file
+    with open(file_path, "wb") as buffer:
+        shutil.copyfileobj(video_file.file, buffer)
+    
+    # Store the path for the streaming endpoint
+    video_sessions[session_id] = file_path
+    
+    return JSONResponse(content={"session_id": session_id})
+
+def generate_upload_frames(session_id: str):
+    """Generates annotated frames from an uploaded video."""
+    file_path = video_sessions.get(session_id)
+    if not file_path or not os.path.exists(file_path):
+        print(f"Error: Video for session {session_id} not found.")
+        return
+
+    video = cv2.VideoCapture(file_path)
+    while video.isOpened():
+        success, frame = video.read()
+        if not success:
+            break
+        else:
+            # Run YOLOv8 inference
+            results = model(frame, classes=[12, 16], verbose=False)
+            annotated_frame = results[0].plot()
+            
+            # Encode frame
+            ret, buffer = cv2.imencode('.jpg', annotated_frame)
+            if not ret:
+                continue
+            frame_bytes = buffer.tobytes()
+
+            yield (b'--frame\r\n'
+                   b'Content-Type: image/jpeg\r\n\r\n' + frame_bytes + b'\r\n')
+    
+    video.release()
+    # Clean up: remove the video file and session entry after streaming
+    if os.path.exists(file_path):
+        os.remove(file_path)
+    if session_id in video_sessions:
+        del video_sessions[session_id]
+
+@app.get("/video_feed/upload")
+def video_feed_upload(session_id: str):
+    """Streams video from an uploaded file based on the session ID."""
+    # Use StreamingResponse for generator functions
+    return StreamingResponse(generate_upload_frames(session_id), media_type="multipart/x-mixed-replace; boundary=frame")

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5464f555f1b9831e6f1f9adcab11063f9a081d62e6650c2f2154bd4c01720836
+size 6249635

inference.py

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+import cv2
+from ultralytics import YOLO
+
+# Load your custom-trained model
+model = YOLO('best.pt')
+
+# To use a video file instead of a webcam, uncomment the line below
+# and replace 'path/to/your/video.mp4' with your video's path.
+cap = cv2.VideoCapture('test1.mp4')
+
+# To use the webcam
+#cap = cv2.VideoCapture(0)
+
+while True:
+    # Read a frame from the camera
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    # Run inference on the frame
+    # We only care about detecting helmets (class 12) and vests (class 16)
+    results = model(frame, classes=[12, 16])
+
+    # Plot the results on the frame
+    annotated_frame = results[0].plot()
+
+    # Display the resulting frame
+    cv2.imshow('Workplace Safety Monitoring', annotated_frame)
+
+    # Press 'q' to exit the loop
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+# Release the camera and destroy all windows
+cap.release()
+cv2.destroyAllWindows()

requirements.txt

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+fastapi
+uvicorn[standard]
+python-multipart
+ultralytics
+opencv-python-headless
+opencv-python

templates/index.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Workplace Safety Detection</title>
+    <script src="https://cdn.tailwindcss.com"></script>
+    <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@400;500;600;700&display=swap" rel="stylesheet">
+    <style>
+        body {
+            font-family: 'Inter', sans-serif;
+        }
+        .btn {
+            @apply flex items-center justify-center w-full px-6 py-3 text-white font-semibold rounded-lg shadow-md transition-all duration-300 transform hover:scale-105;
+        }
+        .btn-sm {
+            @apply px-4 py-2 text-sm;
+        }
+        .btn-primary {
+            @apply bg-indigo-600 hover:bg-indigo-700;
+        }
+        .btn-secondary {
+            @apply bg-gray-600 hover:bg-gray-700;
+        }
+        .btn-danger {
+            @apply bg-red-600 hover:bg-red-700;
+        }
+        .icon {
+            @apply w-6 h-6 mr-3;
+        }
+        .icon-sm {
+            @apply w-5 h-5 mr-2;
+        }
+        /* Simple CSS loader */
+        .loader {
+            border: 4px solid #f3f3f3; /* Light grey */
+            border-top: 4px solid #4f46e5; /* Indigo */
+            border-radius: 50%;
+            width: 40px;
+            height: 40px;
+            animation: spin 1s linear infinite;
+            margin: 20px auto;
+        }
+        @keyframes spin {
+            0% { transform: rotate(0deg); }
+            100% { transform: rotate(360deg); }
+        }
+    </style>
+</head>
+<body class="bg-gradient-to-br from-gray-50 to-gray-200 flex items-center justify-center min-h-screen p-4">
+
+    <div class="bg-white p-8 rounded-xl shadow-2xl max-w-2xl w-full text-center">
+        
+        <!-- Header Section -->
+        <div id="header" class="mb-8">
+            <h1 class="text-3xl font-bold text-gray-800 mb-2">Workplace Safety Detection</h1>
+            <p class="text-gray-500">AI-powered monitoring for helmets and vests.</p>
+        </div>
+
+        <!-- Initial Start Button -->
+        <div id="start-container">
+            <button id="start-btn" class="btn btn-primary text-lg">
+                <svg class="icon" xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor"><path stroke-linecap="round" stroke-linejoin="round" d="M5.25 5.653c0-.856.917-1.398 1.667-.986l11.54 6.348a1.125 1.125 0 010 1.972l-11.54 6.347a1.125 1.125 0 01-1.667-.986V5.653z" /></svg>
+                Start Monitoring
+            </button>
+        </div>
+
+        <!-- Source Selection (hidden by default) -->
+        <div id="source-selection" class="hidden space-y-4">
+            <button id="webcam-btn" class="btn btn-primary">
+                <svg class="icon" xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor"><path stroke-linecap="round" stroke-linejoin="round" d="M6.827 6.175A2.31 2.31 0 015.186 7.23c-.38.054-.757.112-1.134.175C2.999 7.58 2.25 8.507 2.25 9.574V18a2.25 2.25 0 002.25 2.25h15A2.25 2.25 0 0021.75 18V9.574c0-1.067-.75-1.994-1.802-2.169a47.865 47.865 0 00-1.134-.175 2.31 2.31 0 01-1.64-1.055l-.822-1.316a2.192 2.192 0 00-1.736-1.039 48.776 48.776 0 00-5.232 0 2.192 2.192 0 00-1.736 1.039l-.821 1.316z" /><path stroke-linecap="round" stroke-linejoin="round" d="M16.5 12.75a4.5 4.5 0 11-9 0 4.5 4.5 0 019 0zM18.75 10.5h.008v.008h-.008V10.5z" /></svg>
+                Use Webcam
+            </button>
+            <input type="file" id="video-upload-input" class="hidden" accept="video/*">
+            <button id="upload-btn" class="btn btn-secondary">
+                <svg class="icon" xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor"><path stroke-linecap="round" stroke-linejoin="round" d="M3 16.5v2.25A2.25 2.25 0 005.25 21h13.5A2.25 2.25 0 0021 18.75V16.5m-13.5-9L12 3m0 0l4.5 4.5M12 3v13.5" /></svg>
+                Upload a Video
+            </button>
+        </div>
+
+        <!-- Loading Spinner (hidden by default) -->
+        <div id="loading-container" class="hidden">
+            <div class="loader"></div>
+            <p class="text-gray-600">Processing video...</p>
+        </div>
+
+        <!-- Video Display Area (hidden by default) -->
+        <div id="video-container" class="hidden mt-6">
+            <div id="image-wrapper" class="relative w-full bg-black rounded-lg">
+                <img id="video-feed" class="rounded-lg shadow-inner border-2 border-gray-200 w-full" alt="Video Feed">
+                <canvas id="pause-canvas" class="hidden absolute top-0 left-0 rounded-lg w-full h-full"></canvas>
+            </div>
+            <!-- Controls -->
+            <div id="controls-container" class="mt-4 flex justify-center space-x-4">
+                <button id="back-btn" class="btn btn-sm btn-danger">
+                    <svg class="icon-sm" xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor"><path stroke-linecap="round" stroke-linejoin="round" d="M9 15L3 9m0 0l6-6M3 9h12a6 6 0 010 12h-3" /></svg>
+                    Back
+                </button>
+                <div id="video-controls" class="hidden flex space-x-4">
+                    <button id="play-pause-btn" class="btn btn-sm btn-primary">
+                        <svg class="icon-sm" xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor"><path stroke-linecap="round" stroke-linejoin="round" d="M15.75 5.25v13.5m-7.5-13.5v13.5" /></svg>
+                        Pause
+                    </button>
+                    <button id="new-upload-btn" class="btn btn-sm btn-secondary">
+                        <svg class="icon-sm" xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor"><path stroke-linecap="round" stroke-linejoin="round" d="M3 16.5v2.25A2.25 2.25 0 005.25 21h13.5A2.25 2.25 0 0021 18.75V16.5m-13.5-9L12 3m0 0l4.5 4.5M12 3v13.5" /></svg>
+                        New Video
+                    </button>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <script>
+        // --- Element References ---
+        const startContainer = document.getElementById('start-container');
+        const sourceSelection = document.getElementById('source-selection');
+        const loadingContainer = document.getElementById('loading-container');
+        const videoContainer = document.getElementById('video-container');
+        const imageWrapper = document.getElementById('image-wrapper');
+        const videoFeed = document.getElementById('video-feed');
+        const pauseCanvas = document.getElementById('pause-canvas');
+        const videoControls = document.getElementById('video-controls');
+        
+        const startBtn = document.getElementById('start-btn');
+        const webcamBtn = document.getElementById('webcam-btn');
+        const uploadBtn = document.getElementById('upload-btn');
+        const videoUploadInput = document.getElementById('video-upload-input');
+        const backBtn = document.getElementById('back-btn');
+        const playPauseBtn = document.getElementById('play-pause-btn');
+        const newUploadBtn = document.getElementById('new-upload-btn');
+
+        // --- State Management ---
+        let currentVideoSrc = '';
+        let isPaused = false;
+
+        // --- UI Control Functions ---
+        function showScreen(screen) {
+            startContainer.classList.add('hidden');
+            sourceSelection.classList.add('hidden');
+            loadingContainer.classList.add('hidden');
+            videoContainer.classList.add('hidden');
+            screen.classList.remove('hidden');
+        }
+
+        function resetToHome() {
+            videoFeed.src = ''; // Stop any active stream
+            pauseCanvas.classList.add('hidden');
+            videoControls.classList.add('hidden');
+            imageWrapper.style.height = ''; // Reset fixed height
+            showScreen(sourceSelection);
+        }
+
+        // --- Event Listeners ---
+        startBtn.addEventListener('click', () => showScreen(sourceSelection));
+        
+        backBtn.addEventListener('click', resetToHome);
+
+        webcamBtn.addEventListener('click', () => {
+            showScreen(loadingContainer);
+            videoFeed.classList.remove('hidden');
+            videoFeed.src = '/video_feed/webcam';
+            videoFeed.onload = () => showScreen(videoContainer);
+        });
+
+        uploadBtn.addEventListener('click', () => videoUploadInput.click());
+        newUploadBtn.addEventListener('click', () => videoUploadInput.click());
+
+        videoUploadInput.addEventListener('change', (event) => {
+            const file = event.target.files[0];
+            if (file) {
+                showScreen(loadingContainer);
+                const formData = new FormData();
+                formData.append('video_file', file);
+
+                fetch('/upload_video', { method: 'POST', body: formData })
+                .then(response => {
+                    if (!response.ok) throw new Error('Upload failed.');
+                    return response.json();
+                })
+                .then(data => {
+                    currentVideoSrc = `/video_feed/upload?session_id=${data.session_id}`;
+                    videoFeed.classList.remove('hidden');
+                    videoFeed.src = currentVideoSrc;
+                    isPaused = false;
+                    playPauseBtn.innerHTML = '<svg class="icon-sm" xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor"><path stroke-linecap="round" stroke-linejoin="round" d="M15.75 5.25v13.5m-7.5-13.5v13.5" /></svg>Pause';
+                    videoControls.classList.remove('hidden');
+                    videoFeed.onload = () => showScreen(videoContainer);
+                })
+                .catch(error => {
+                    console.error('Error:', error);
+                    alert('There was an error uploading your video.');
+                    showScreen(sourceSelection);
+                });
+            }
+        });
+
+        playPauseBtn.addEventListener('click', () => {
+            if (isPaused) {
+                // --- Resume Logic ---
+                pauseCanvas.classList.add('hidden');
+                imageWrapper.style.height = ''; // Remove fixed height
+                videoFeed.classList.remove('hidden');
+                videoFeed.src = currentVideoSrc; // Restart the stream
+                playPauseBtn.innerHTML = '<svg class="icon-sm" xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor"><path stroke-linecap="round" stroke-linejoin="round" d="M15.75 5.25v13.5m-7.5-13.5v13.5" /></svg>Pause';
+                isPaused = false;
+            } else {
+                // --- Pause Logic ---
+                // Set fixed height on wrapper to prevent layout collapse
+                imageWrapper.style.height = `${videoFeed.clientHeight}px`;
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+                const ctx = pauseCanvas.getContext('2d');
+                pauseCanvas.width = videoFeed.clientWidth;
+                pauseCanvas.height = videoFeed.clientHeight;
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+                
+                videoFeed.classList.add('hidden'); // Hide the img tag
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+                videoFeed.src = ''; // Stop the stream
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+                playPauseBtn.innerHTML = '<svg class="icon-sm" xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor"><path stroke-linecap="round" stroke-linejoin="round" d="M5.25 5.653c0-.856.917-1.398 1.667-.986l11.54 6.348a1.125 1.125 0 010 1.972l-11.54 6.347a1.125 1.125 0 01-1.667-.986V5.653z" /></svg>Resume';
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+</html>

workplace-safety.ipynb

@@ -0,0 +1 @@
+{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":12475735,"sourceType":"datasetVersion","datasetId":7871309}],"dockerImageVersionId":31090,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install ultralytics opencv-python","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"%%writefile data.yaml\n\ntrain: /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images\nval: /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/valid/images\ntest: /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/test/images\n\nnc: 17\nnames:\n  - 'Barefoots'\n  - 'Ear-protection'\n  - 'Harness'\n  - 'No_Ear-Protection'\n  - 'No_Glasses'\n  - 'Sandals'\n  - 'boots'\n  - 'face_mask'\n  - 'face_nomask'\n  - 'glasses'\n  - 'hand_glove'\n  - 'hand_noglove'\n  - 'head_helmet'\n  - 'head_nohelmet'\n  - 'person'\n  - 'shoes'\n  - 'vest'","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-15T10:37:37.034389Z","iopub.execute_input":"2025-07-15T10:37:37.034953Z","iopub.status.idle":"2025-07-15T10:37:37.040386Z","shell.execute_reply.started":"2025-07-15T10:37:37.034925Z","shell.execute_reply":"2025-07-15T10:37:37.039539Z"}},"outputs":[{"name":"stdout","text":"Overwriting data.yaml\n","output_type":"stream"}],"execution_count":5},{"cell_type":"code","source":"!yolo detect train data=data.yaml model=yolov8n.pt epochs=10 imgsz=640 batch=16 device=0,1","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-15T11:18:00.156297Z","iopub.execute_input":"2025-07-15T11:18:00.156984Z","iopub.status.idle":"2025-07-15T12:03:21.801048Z","shell.execute_reply.started":"2025-07-15T11:18:00.156954Z","shell.execute_reply":"2025-07-15T12:03:21.800025Z"}},"outputs":[{"name":"stdout","text":"New https://pypi.org/project/ultralytics/8.3.167 available 😃 Update with 'pip install -U ultralytics'\nUltralytics 8.3.166 🚀 Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (Tesla T4, 15095MiB)\n                                                        CUDA:1 (Tesla T4, 15095MiB)\n\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, augment=False, auto_augment=randaugment, batch=16, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=data.yaml, degrees=0.0, deterministic=True, device=0,1, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=10, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.01, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolov8n.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=train4, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=runs/detect/train4, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\nOverriding model.yaml nc=80 with nc=17\n\n                   from  n    params  module                                       arguments                     \n  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 \n  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                \n  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             \n  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             \n  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           \n  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           \n  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 \n 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 \n 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  \n 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 \n 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 \n 22        [15, 18, 21]  1    754627  ultralytics.nn.modules.head.Detect           [17, [64, 128, 256]]          \nModel summary: 129 layers, 3,014,163 parameters, 3,014,147 gradients, 8.2 GFLOPs\n\nTransferred 319/355 items from pretrained weights\n\u001b[34m\u001b[1mDDP:\u001b[0m debug command /usr/bin/python3 -m torch.distributed.run --nproc_per_node 2 --master_port 56587 /root/.config/Ultralytics/DDP/_temp_sasvsj33131961272156368.py\nUltralytics 8.3.166 🚀 Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (Tesla T4, 15095MiB)\n                                                        CUDA:1 (Tesla T4, 15095MiB)\nOverriding model.yaml nc=80 with nc=17\nTransferred 319/355 items from pretrained weights\nFreezing layer 'model.22.dfl.conv.weight'\n\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 38.5±17.2 MB/s, size: 54.1 KB)\n\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i\u001b[0m\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-01-15-1-1-1-1-1-mp40_jpg.rf.d01f57d39ddf7b0da68e999207153d2f.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-01-15-1-1-1-1-1-mp42_jpg.rf.283e69143e7c30f0faae2a0dff17362c.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-01-15-1-1-1-1-112_jpg.rf.59003700ede444f10f0295de3f9c564c.jpg: 2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-01-15-1-1-1-1-112_jpg.rf.7b39ff350b7ad1eccef20195f19337c7.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe 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2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-01-15-1-3-1-5-29_jpg.rf.e7dc20dc5ab52fd8d0ca6851cb61ec07.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-01-15-1-5-1-2-110_jpg.rf.8deac143d2830e72ef7503498063c4b6.jpg: 2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-01-15-1-5-1-2-26_jpg.rf.2d258edb7ed3a3f823e43921b03c4e8f.jpg: 4 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-01-15-1-5-1-4-212_jpg.rf.30a305d85a2a13f88d62ddf94ca95594.jpg: 2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe 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detection.v3i.yolov8/train/images/gettyimages-149319933-612x612_jpg.rf.cd464833d2864f81b507a2345380219a.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-149319933-612x612_jpg.rf.f5664c32c49a80d434a30ab00ce30717.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-149320119-612x612_jpg.rf.240d6fd4a0307bfbe077d520971ce536.jpg: 7 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-149320119-612x612_jpg.rf.29d7a331c96dda77786941c549d66fd7.jpg: 5 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-149320119-612x612_jpg.rf.8767c75438479dcc2fbfce3cc8d27ce7.jpg: 9 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-508121319-612x612_jpg.rf.21131d80940e399df883589112ad6d68.jpg: 2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-508121319-612x612_jpg.rf.457fddf239930a0e8bc659469e4584d4.jpg: 2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-508121319-612x612_jpg.rf.64209e6a474da8327dfa6e9ef4847bba.jpg: 6 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-514366692-612x612_jpg.rf.6c32f15192c2abf718b3e32d8ec38c7e.jpg: 4 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-514366692-612x612_jpg.rf.f9e5b06e998d262e47f22443f9cac80f.jpg: 4 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-516012290-612x612_jpg.rf.1072ef227298a8411cefdb6c070769f6.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-516012290-612x612_jpg.rf.30add10c2cad696bd2f84faa05d9016a.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-516012290-612x612_jpg.rf.d49ebdaa954c0a7a76853418c0b5d055.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-691703536-612x612_jpg.rf.0e3960ee2af61a1173506c59bc05fccb.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-691703536-612x612_jpg.rf.759948f30c09ae9117cf112d4962886b.jpg: 2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-691703536-612x612_jpg.rf.db435f1dd5ee84b9790f08637b349bd9.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-807680780-612x612_jpg.rf.6b27b5fa7e895ba681cfbdee5888e7ed.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-807680780-612x612_jpg.rf.c39aed43a5e4c129c71cc2039ac2e32e.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-807680780-612x612_jpg.rf.cffdda45225d80748bac9acd828a1586.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-916731436-612x612_jpg.rf.4cb1fe65109500b175ef9dda6ec5b149.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-916731436-612x612_jpg.rf.ae29b471e1b73df861499d978154b84c.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-916731436-612x612_jpg.rf.e1b7bcaad9ffcdc683c574780e370e1c.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-950956144-612x612_jpg.rf.0118aba0f13fd807f403809332470d45.jpg: 4 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-950956144-612x612_jpg.rf.3f935a9327167c8b626cac8e4f843bc5.jpg: 2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-950956144-612x612_jpg.rf.ba8e76badfca8313f71b0e3433c3a614.jpg: 4 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/hard_hat_workers1041_png.rf.1d7372cb698a25bcc0336480c681008a.jpg: 4 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/image_209_jpg.rf.06b4002494b1dbf38541344c6573786e.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/imagem_processada_1_jpg.rf.5c33212824cdee5ac2d699f1872d4ca9.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/images-14-_jpg.rf.5f953c6e5802a23ba82fe19887e6b586.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/images-14-_jpg.rf.7fcbefa1605bc3bdd63b556d1375ecf7.jpg: 2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/images172_jpg.rf.87a3c988463a031ebff7015439748996.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/images172_jpg.rf.c98d1a5f256e6f0edbe933bac097a0c1.jpg: 7 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/images254_jpg.rf.f0e8b95f92a5d93adf4e3c09eb9007c6.jpg: 5 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/images27_jpg.rf.d75234fcb95b13b146be0875e3b0b342.jpg: 4 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/istockphoto-1040921246-612x612_jpg.rf.c325c156a0f79a8e3759fd38fc5ed2e5.jpg: 2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/istockphoto-1301543637-612x612_jpg.rf.0602568f4aaa2771f703c4c1da883c8f.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/istockphoto-92003976-612x612_jpg.rf.20775ce01cd3e701b2373c07b68a9a99.jpg: 5 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/youtube-109_jpg.rf.d2906848f52cf8bf5dd36d1e5c7f2025.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/youtube-52_jpg.rf.849c4f137696732bd810d2a4dcfe3c86.jpg: 2 duplicate labels removed\nWARNING ⚠️ \u001b[34m\u001b[1mtrain: \u001b[0mCache directory /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train is not writeable, cache not saved.\nWARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 41264, len(boxes) = 219348. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\n\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i\u001b[0m\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.4±0.8 ms, read: 42.6±18.9 MB/s, size: 51.5 KB)\n\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.y\u001b[0m\nWARNING ⚠️ \u001b[34m\u001b[1mval: \u001b[0mCache directory /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/valid is not writeable, cache not saved.\nWARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 333, len(boxes) = 8996. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\n\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i\u001b[0mPlotting labels to runs/detect/train4/labels.jpg... \n\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i\u001b[0m\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.000476, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\nImage sizes 640 train, 640 val\nUsing 4 dataloader workers\nLogging results to \u001b[1mruns/detect/train4\u001b[0m\nStarting training for 10 epochs...\nClosing dataloader mosaic\n\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i\u001b[0m\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i\u001b[0m\n       1/10      1.35G      1.555      2.599      1.607          9        640: 1\n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all       1636       8996      0.537      0.522      0.518      0.289\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n       2/10      1.67G      1.442       1.88      1.512         13        640: 1\n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all       1636       8996       0.62      0.574      0.584      0.335\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n       3/10      1.69G        1.4      1.711      1.481         13        640: 1\n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all       1636       8996      0.639       0.61      0.619      0.361\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n       4/10       1.7G      1.362       1.61      1.458         20        640: 1\n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all       1636       8996      0.673      0.623      0.646      0.381\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n       5/10      1.72G      1.319      1.506      1.419         10        640: 1\n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all       1636       8996      0.685      0.654      0.665      0.396\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n       6/10      1.73G      1.284       1.43      1.391         24        640: 1\n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all       1636       8996      0.666      0.683      0.678      0.412\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n       7/10      1.75G      1.264      1.381      1.377         10        640: 1\n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all       1636       8996        0.7      0.691      0.709       0.43\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n       8/10      1.77G      1.238      1.329      1.354         14        640: 1\n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all       1636       8996      0.713       0.69      0.718      0.439\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n       9/10      1.79G      1.208       1.28      1.336         22        640: 1\n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all       1636       8996        0.7      0.722      0.726      0.448\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      10/10       1.8G      1.189       1.24      1.322         21        640: 1\n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all       1636       8996      0.719      0.714      0.734      0.456\n\n10 epochs completed in 0.720 hours.\nOptimizer stripped from runs/detect/train4/weights/last.pt, 6.2MB\nOptimizer stripped from runs/detect/train4/weights/best.pt, 6.2MB\n\nValidating runs/detect/train4/weights/best.pt...\nUltralytics 8.3.166 🚀 Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (Tesla T4, 15095MiB)\n                                                        CUDA:1 (Tesla T4, 15095MiB)\nModel summary (fused): 72 layers, 3,008,963 parameters, 0 gradients, 8.1 GFLOPs\n                 Class     Images  Instances      Box(P          R      mAP50  m\n/usr/local/lib/python3.11/dist-packages/matplotlib/colors.py:721: RuntimeWarning: invalid value encountered in less\n  xa[xa < 0] = -1\n/usr/local/lib/python3.11/dist-packages/matplotlib/colors.py:721: RuntimeWarning: invalid value encountered in less\n  xa[xa < 0] = -1\n                   all       1636       8996       0.72      0.715      0.735      0.456\n             Barefoots        172        327      0.861      0.914       0.95      0.668\n        Ear-protection        153        235      0.518      0.586      0.552       0.34\n               Harness        237        347      0.719      0.738      0.772      0.476\n     No_Ear-Protection        115        153       0.43       0.34       0.36      0.205\n            No_Glasses         80         95      0.494      0.568      0.482      0.244\n               Sandals        139        199      0.684      0.884      0.801      0.501\n                 boots        313        661       0.92       0.82      0.903      0.662\n             face_mask        309        399      0.733      0.797      0.748      0.384\n           face_nomask        359        563      0.753      0.726      0.783       0.39\n               glasses        328        401      0.757      0.739      0.742      0.411\n            hand_glove        449        830      0.774      0.657      0.672      0.392\n          hand_noglove        305        620      0.682      0.373      0.496      0.228\n           head_helmet        436        704      0.784      0.824      0.866      0.584\n         head_nohelmet        432        846      0.832      0.817      0.874      0.535\n                person        831       1488      0.782       0.88      0.895      0.697\n                 shoes        232        547      0.667       0.59      0.664      0.333\n                  vest        398        581      0.841      0.897      0.935      0.705\nSpeed: 0.2ms preprocess, 2.1ms inference, 0.0ms loss, 1.5ms postprocess per image\nResults saved to \u001b[1mruns/detect/train4\u001b[0m\n💡 Learn more at https://docs.ultralytics.com/modes/train\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}