diff --git a/submissions/T-075_Turinators/T-075_Turinators_Presentation.pdf b/submissions/T-075_Turinators/T-075_Turinators_Presentation.pdf new file mode 100644 index 00000000..983e59d9 Binary files /dev/null and b/submissions/T-075_Turinators/T-075_Turinators_Presentation.pdf differ diff --git a/submissions/T-075_Turinators/code/Test Code b/submissions/T-075_Turinators/code/Test Code new file mode 100644 index 00000000..8ce863f0 --- /dev/null +++ b/submissions/T-075_Turinators/code/Test Code @@ -0,0 +1,10 @@ +from ultralytics import YOLO + +model = YOLO('best _new.pt') + +input_image_path = 'test2[1].jpg' # Apni image ka naam yahan likhein + +results = model(input_image_path) + +print("Prediction is complete.") +results[0].show() diff --git a/submissions/T-075_Turinators/code/backend/.gitignore b/submissions/T-075_Turinators/code/backend/.gitignore new file mode 100644 index 00000000..1a14a62d --- /dev/null +++ b/submissions/T-075_Turinators/code/backend/.gitignore @@ -0,0 +1,15 @@ +# Created by venv; see https://docs.python.org/3/library/venv.html + +__pycache__/ +*.pyc +venv/ +.env/ +.venv/ +pyvenv.cfg +Scripts +Lib +Include +share +temp_frames +uploads +outputs \ No newline at end of file diff --git a/submissions/T-075_Turinators/code/backend/app.py b/submissions/T-075_Turinators/code/backend/app.py new file mode 100644 index 00000000..f9e1a668 --- /dev/null +++ b/submissions/T-075_Turinators/code/backend/app.py @@ -0,0 +1,49 @@ +# Importing flask module in the project is mandatory +# An object of Flask class is our WSGI application. +from flask import Flask, request, jsonify, send_file, Response, send_from_directory +import os +from flask_cors import CORS +import test + +# Flask constructor takes the name of +# current module (__name__) as argument. +app = Flask(__name__) +UPLOAD_FOLDER = "uploads" +os.makedirs(UPLOAD_FOLDER, exist_ok=True) +OUTPUT_FOLDER = "outputs" +os.makedirs(OUTPUT_FOLDER, exist_ok=True) +CORS(app) +# The route() function of the Flask class is a decorator, +# which tells the application which URL should call +# the associated function. +@app.route('/') +# ‘/’ URL is bound with hello_world() function. +def hello_world(): + return 'Hello World!!' + +@app.route('/upload', methods=['POST']) +def upload(): + if "video" not in request.files: + return jsonify({"error": "No video uploaded"}), 400 + + video = request.files["video"] + filename = video.filename + save_path = os.path.join(UPLOAD_FOLDER, filename) + video.save(save_path) + print("Video saved at", save_path) + output_path = test.process_video(filename, "best_new.pt") + return jsonify({"url": f"http://localhost:5000/output/{output_path}"}) + + +@app.route('/output/') +def serve_output(filename): + print(filename) + return send_file(os.path.join('outputs', filename), as_attachment=True) + + +# main driver function +if __name__ == '__main__': + + # run() method of Flask class runs the application + # on the local development server. + app.run(debug=True) \ No newline at end of file diff --git a/submissions/T-075_Turinators/code/backend/best_new.pt b/submissions/T-075_Turinators/code/backend/best_new.pt new file mode 100644 index 00000000..498b3af6 Binary files /dev/null and b/submissions/T-075_Turinators/code/backend/best_new.pt differ diff --git a/submissions/T-075_Turinators/code/backend/bestn.pt b/submissions/T-075_Turinators/code/backend/bestn.pt new file mode 100644 index 00000000..44848d21 Binary files /dev/null and b/submissions/T-075_Turinators/code/backend/bestn.pt differ diff --git a/submissions/T-075_Turinators/code/backend/requirements.txt b/submissions/T-075_Turinators/code/backend/requirements.txt new file mode 100644 index 00000000..262d8fa8 --- /dev/null +++ b/submissions/T-075_Turinators/code/backend/requirements.txt @@ -0,0 +1,4 @@ +ultralytics +numpy +opencv-python +flask \ No newline at end of file diff --git a/submissions/T-075_Turinators/code/backend/templates/index.html b/submissions/T-075_Turinators/code/backend/templates/index.html new file mode 100644 index 00000000..1db3cd9a --- /dev/null +++ b/submissions/T-075_Turinators/code/backend/templates/index.html @@ -0,0 +1,68 @@ + + + + + + Vahan Sarthi + + + + + + + +
+ +
+
+ + + + Upload Code +
+
+ About Us +
+
+ Dataset +
+
+ + + +
+
+ + +
+ +
+ + +
+ +
+ + + + + +
+
+ + + +
+
+ + + + diff --git a/submissions/T-075_Turinators/code/backend/templates/script.js b/submissions/T-075_Turinators/code/backend/templates/script.js new file mode 100644 index 00000000..5c954f4c --- /dev/null +++ b/submissions/T-075_Turinators/code/backend/templates/script.js @@ -0,0 +1,83 @@ +// State management +let selectedFile = null; +let loading = false; + +// DOM elements +const videoInput = document.getElementById('video-input'); +const uploadBtn = document.getElementById('upload-btn'); +const uploadContent = document.getElementById('upload-content'); +const loadingSection = document.getElementById('loading-section'); +const outputSection = document.getElementById('output-section'); +const outputVideo = document.getElementById('output-video'); +const videoSource = document.getElementById('video-source'); +const toastContainer = document.getElementById('toast-container'); + +// Event listeners +videoInput.addEventListener('change', (e) => { + selectedFile = e.target.files[0] || null; +}); + +uploadBtn.addEventListener('click', handleUpload); + +// Toast function +function showToast(message, duration = 3000) { + const toast = document.createElement('div'); + toast.className = 'toast'; + toast.textContent = message; + + toastContainer.appendChild(toast); + + setTimeout(() => { + toast.classList.add('fade-out'); + setTimeout(() => { + toastContainer.removeChild(toast); + }, 300); + }, duration); +} + +// Handle file upload +async function handleUpload() { + if (!selectedFile) { + showToast('Please select a file'); + return; + } + + setLoading(true); + + const formData = new FormData(); + formData.append('video', selectedFile); + + try { + const response = await fetch('http://localhost:5000/upload', { + method: 'POST', + body: formData + }); + + const data = await response.json(); + console.log(data); + + // Display output video + videoSource.src = data.url; + outputVideo.load(); + outputSection.style.display = 'block'; + + showToast('Upload successful!'); + } catch (error) { + console.error('Upload error:', error); + showToast('Upload failed. Please try again.'); + } finally { + setLoading(false); + } +} + +// Toggle loading state +function setLoading(isLoading) { + loading = isLoading; + if (isLoading) { + uploadContent.style.display = 'none'; + loadingSection.style.display = 'block'; + } else { + uploadContent.style.display = 'flex'; + loadingSection.style.display = 'none'; + } +} diff --git a/submissions/T-075_Turinators/code/backend/templates/style.css b/submissions/T-075_Turinators/code/backend/templates/style.css new file mode 100644 index 00000000..c469da47 --- /dev/null +++ b/submissions/T-075_Turinators/code/backend/templates/style.css @@ -0,0 +1,163 @@ +* { + margin: 0; + padding: 0; + box-sizing: border-box; +} + +body { + font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif; + background-color: #ffffff; + color: #000000; +} + +/* Navbar */ +.navbar { + display: flex; + align-items: center; + gap: 8px; + font-size: 24px; + background-color: #f3f4f6; + padding: 12px 16px; +} + +/* Main Container */ +.main-container { + display: flex; + width: 100%; +} + +/* Sidebar */ +.sidebar { + display: flex; + flex-direction: column; + background-color: #f3f4f6; + gap: 8px; + padding: 8px; + min-height: calc(100vh - 56px); +} + +.sidebar-item { + padding: 8px; + cursor: pointer; + display: flex; + align-items: center; + gap: 8px; + transition: background-color 0.2s; +} + +.sidebar-item:hover { + background-color: #e5e7eb; + border-radius: 4px; +} + +/* App Content */ +.app-content { + background-color: #ffffff; + color: #000000; + width: 100%; + min-height: calc(100vh - 56px); + display: flex; + flex-direction: column; + align-items: center; + padding: 32px 0; + position: relative; +} + +/* Upload Section */ +.upload-section { + background-color: #e5e7eb; + border-radius: 12px; + width: 400px; + height: 200px; + display: flex; + align-items: center; + justify-content: center; + margin-bottom: 32px; +} + +#upload-content { + display: flex; + flex-direction: column; + align-items: center; + gap: 16px; +} + +#loading-section { + font-size: 18px; + font-weight: 500; +} + +#video-input { + padding: 8px; +} + +.upload-button { + background-color: #14b8a6; + color: white; + font-weight: bold; + padding: 8px 16px; + border-radius: 4px; + border: none; + cursor: pointer; + transition: background-color 0.2s; +} + +.upload-button:hover { + background-color: #0d9488; +} + +/* Output Section */ +.output-section { + margin-top: 32px; + padding: 16px; + background-color: #e5e7eb; + border-radius: 24px; +} + +#output-video { + border-radius: 16px; +} + +/* Toast */ +#toast-container { + position: fixed; + top: 16px; + right: 16px; + z-index: 50; +} + +.toast { + background-color: #1f2937; + color: white; + padding: 8px 16px; + border-radius: 8px; + box-shadow: 0 10px 15px -3px rgba(0, 0, 0, 0.1); + border: 1px solid #374151; + max-width: 320px; + margin-bottom: 8px; + animation: slideIn 0.3s ease-out; +} + +@keyframes slideIn { + from { + transform: translateX(100%); + opacity: 0; + } + to { + transform: translateX(0); + opacity: 1; + } +} + +.toast.fade-out { + animation: fadeOut 0.3s ease-out forwards; +} + +@keyframes fadeOut { + from { + opacity: 1; + } + to { + opacity: 0; + } +} diff --git a/submissions/T-075_Turinators/code/backend/test.py b/submissions/T-075_Turinators/code/backend/test.py new file mode 100644 index 00000000..294c9d38 --- /dev/null +++ b/submissions/T-075_Turinators/code/backend/test.py @@ -0,0 +1,186 @@ +import cv2 +import os +import shutil +from ultralytics import YOLO +import numpy as np + +def process_video(input_path, model_name, frame: int = 6): + UPLOAD_FOLDER = "uploads" + os.makedirs(UPLOAD_FOLDER, exist_ok=True) + OUTPUT_FOLDER = "outputs" + os.makedirs(OUTPUT_FOLDER, exist_ok=True) + + model_path = model_name + video_path = os.path.join(UPLOAD_FOLDER, input_path) + output_video_path = os.path.join(OUTPUT_FOLDER, input_path) + temp_dir = os.path.join(OUTPUT_FOLDER, "temp_frames") + + fps_extract = frame + + model = YOLO(model_path) + + if os.path.exists(temp_dir): + shutil.rmtree(temp_dir) + os.makedirs(temp_dir) + + cap = cv2.VideoCapture(video_path) + fps = cap.get(cv2.CAP_PROP_FPS) + frame_interval = int(fps / fps_extract) + + frame_count = 0 + saved_count = 0 + + while True: + ret, frame = cap.read() + if not ret: + break + if frame_count % frame_interval == 0: + frame_path = os.path.join(temp_dir, f"frame_{saved_count:05d}.jpg") + cv2.imwrite(frame_path, frame) + saved_count += 1 + frame_count += 1 + + cap.release() + print(f" Extracted {saved_count} frames into {temp_dir}") + + processed_dir = os.path.join(temp_dir, "processed") + os.makedirs(processed_dir, exist_ok=True) + + for filename in sorted(os.listdir(temp_dir)): + if not filename.endswith(".jpg"): + continue + frame_path = os.path.join(temp_dir, filename) + results = model(frame_path) + + + # Get frame dimensions + frame_yolo = results[0].plot() + height, width = frame_yolo.shape[:2] + mid_x = width // 2 + + # Check if masks exist + if results[0].masks is not None: + pothole_data = [] + + # Collect polygon data for each detection + for i, poly in enumerate(results[0].masks.xy): + polygon = np.array(poly, dtype=np.int32) # (N,2) points + area = cv2.contourArea(polygon) + + # Calculate centroid of polygon + M = cv2.moments(polygon) + if M["m00"] != 0: + cx = int(M["m10"] / M["m00"]) + cy = int(M["m01"] / M["m00"]) + else: + cx, cy = polygon[0] + + # Determine side (left or right of center) + side = "left" if cx < mid_x else "right" + + pothole_data.append({ + 'index': i, + 'polygon': polygon, + 'area': area, + 'centroid': (cx, cy), + 'side': side + }) + + print(f"Polygon {i+1} → Area: {area:.2f} pixels² | Side: {side}") + + # Count potholes in vertical 75% region (top to 75% of height) + vertical_75_threshold = int(height * 0.75) + potholes_in_75 = [p for p in pothole_data if p['centroid'][1] <= vertical_75_threshold] + + # CASE 3: More than 3 potholes in front 75% vertical region + if len(potholes_in_75) >= 3: + cv2.putText(frame_yolo, "SLOW SPEED", + (width//2 - 150, 80), + cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 4) + cv2.putText(frame_yolo, " MULTIPLE POTHOLES AHEAD", + (width//2 - 250, 140), + cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 165, 255), 2) + + # CASE 1 & 2: Directional guidance + elif len(pothole_data) > 0: + # Separate by side + left_potholes = [p for p in pothole_data if p['side'] == 'left'] + right_potholes = [p for p in pothole_data if p['side'] == 'right'] + + # CASE 1: Only one pothole OR potholes on one side only + if len(left_potholes) == 0 and len(right_potholes) > 0: + # Potholes on right, show arrow pointing left + draw_arrow(frame_yolo, "LEFT", width, height) + + elif len(right_potholes) == 0 and len(left_potholes) > 0: + # Potholes on left, show arrow pointing right + draw_arrow(frame_yolo, "RIGHT", width, height) + + # CASE 2: Potholes on both sides + elif len(left_potholes) > 0 and len(right_potholes) > 0: + # Calculate total area on each side + left_total_area = sum(p['area'] for p in left_potholes) + right_total_area = sum(p['area'] for p in right_potholes) + + # Point away from bigger pothole area + if left_total_area > right_total_area: + draw_arrow(frame_yolo, "RIGHT", width, height) + print(f"Left area ({left_total_area:.2f}) > Right area ({right_total_area:.2f}) → Go RIGHT") + else: + draw_arrow(frame_yolo, "LEFT", width, height) + print(f"Right area ({right_total_area:.2f}) > Left area ({left_total_area:.2f}) → Go LEFT") + + + def draw_arrow(frame, direction, width, height): + """Draw a red directional arrow on the frame""" + arrow_color = (0, 0, 255) # Red in BGR + thickness = 8 + + if direction == "LEFT": + # Draw left arrow in left portion of frame + arrow_start = (int(width * 0.3), height // 2) + arrow_end = (int(width * 0.1), height // 2) + + # Main arrow line + cv2.arrowedLine(frame, arrow_start, arrow_end, + arrow_color, thickness, tipLength=0.5) + + # Text + cv2.putText(frame, "← GO LEFT", + (int(width * 0.05), height // 2 - 30), + cv2.FONT_HERSHEY_SIMPLEX, 1.5, arrow_color, 3) + + elif direction == "RIGHT": + # Draw right arrow in right portion of frame + arrow_start = (int(width * 0.7), height // 2) + arrow_end = (int(width * 0.9), height // 2) + + # Main arrow line + cv2.arrowedLine(frame, arrow_start, arrow_end, + arrow_color, thickness, tipLength=0.5) + + # Text + cv2.putText(frame, "GO RIGHT →", + (int(width * 0.65), height // 2 - 30), + cv2.FONT_HERSHEY_SIMPLEX, 1.5, arrow_color, 3) + + else: + print("No polygons detected") + + cv2.imwrite(os.path.join(processed_dir, filename), frame_yolo) + + print(f" Processed frames saved into {processed_dir}") + + first_frame = cv2.imread(os.path.join(processed_dir, sorted(os.listdir(processed_dir))[0])) + height, width, _ = first_frame.shape + + out = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*'mp4v'), fps_extract, (width, height)) + + for filename in sorted(os.listdir(processed_dir)): + frame_path = os.path.join(processed_dir, filename) + frame = cv2.imread(frame_path) + out.write(frame) + + out.release() + print(f" Output video saved as {output_video_path}") + return input_path diff --git a/submissions/T-075_Turinators/code/frontend/.gitignore b/submissions/T-075_Turinators/code/frontend/.gitignore new file mode 100644 index 00000000..a547bf36 --- /dev/null +++ b/submissions/T-075_Turinators/code/frontend/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/submissions/T-075_Turinators/code/frontend/README.md b/submissions/T-075_Turinators/code/frontend/README.md new file mode 100644 index 00000000..18bc70eb --- /dev/null +++ b/submissions/T-075_Turinators/code/frontend/README.md @@ -0,0 +1,16 @@ +# React + Vite + +This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules. + +Currently, two official plugins are available: + +- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Babel](https://babeljs.io/) (or [oxc](https://oxc.rs) when used in [rolldown-vite](https://vite.dev/guide/rolldown)) for Fast Refresh +- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh + +## React Compiler + +The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation). + +## Expanding the ESLint configuration + +If you are developing a production application, we recommend using TypeScript with type-aware lint rules enabled. Check out the [TS template](https://github.com/vitejs/vite/tree/main/packages/create-vite/template-react-ts) for information on how to integrate TypeScript and [`typescript-eslint`](https://typescript-eslint.io) in your project. diff --git a/submissions/T-075_Turinators/code/frontend/eslint.config.js b/submissions/T-075_Turinators/code/frontend/eslint.config.js new file mode 100644 index 00000000..cee1e2c7 --- /dev/null +++ b/submissions/T-075_Turinators/code/frontend/eslint.config.js @@ -0,0 +1,29 @@ +import js from '@eslint/js' +import globals from 'globals' +import reactHooks from 'eslint-plugin-react-hooks' +import reactRefresh from 'eslint-plugin-react-refresh' +import { defineConfig, globalIgnores } from 'eslint/config' + +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{js,jsx}'], + extends: [ + js.configs.recommended, + reactHooks.configs['recommended-latest'], + reactRefresh.configs.vite, + ], + languageOptions: { + ecmaVersion: 2020, + globals: globals.browser, + parserOptions: { + ecmaVersion: 'latest', + ecmaFeatures: { jsx: true }, + sourceType: 'module', + }, + }, + rules: { + 'no-unused-vars': ['error', { varsIgnorePattern: '^[A-Z_]' }], + }, + }, +]) diff --git a/submissions/T-075_Turinators/code/frontend/index.html b/submissions/T-075_Turinators/code/frontend/index.html new file mode 100644 index 00000000..c20fbd3a --- /dev/null +++ b/submissions/T-075_Turinators/code/frontend/index.html @@ -0,0 +1,13 @@ + + + + + + + frontend + + +
+ + + diff --git a/submissions/T-075_Turinators/code/frontend/package-lock.json b/submissions/T-075_Turinators/code/frontend/package-lock.json new file mode 100644 index 00000000..8cac7209 --- /dev/null +++ b/submissions/T-075_Turinators/code/frontend/package-lock.json @@ -0,0 +1,4381 @@ +{ + "name": "frontend", + "version": "0.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "frontend", + "version": "0.0.0", + "dependencies": { + "@radui/ui": "^0.1.7", + "react": "^19.0.0", + "react-dom": "^19.0.0" + }, + "devDependencies": { + "@eslint/js": "^9.36.0", + "@types/react": "^19.1.16", + "@types/react-dom": "^19.1.9", + "@vitejs/plugin-react": "^5.0.4", + "autoprefixer": "^10.4.21", + "eslint": "^9.36.0", + "eslint-plugin-react-hooks": "^5.2.0", + "eslint-plugin-react-refresh": "^0.4.22", + "globals": "^16.4.0", + "postcss": "^8.5.6", + "tailwindcss": "^3.4.18", + "vite": "^6.3.1" + } + }, + "node_modules/@alloc/quick-lru": { + "version": "5.2.0", + 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}, + "dependencies": { + "@radui/ui": "^0.1.7", + "react": "^19.0.0", + "react-dom": "^19.0.0" + }, + "devDependencies": { + "@eslint/js": "^9.36.0", + "@types/react": "^19.1.16", + "@types/react-dom": "^19.1.9", + "@vitejs/plugin-react": "^5.0.4", + "autoprefixer": "^10.4.21", + "eslint": "^9.36.0", + "eslint-plugin-react-hooks": "^5.2.0", + "eslint-plugin-react-refresh": "^0.4.22", + "globals": "^16.4.0", + "postcss": "^8.5.6", + "tailwindcss": "^3.4.18", + "vite": "^6.3.1" + } +} diff --git a/submissions/T-075_Turinators/code/frontend/postcss.config.js b/submissions/T-075_Turinators/code/frontend/postcss.config.js new file mode 100644 index 00000000..2e7af2b7 --- /dev/null +++ b/submissions/T-075_Turinators/code/frontend/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/submissions/T-075_Turinators/code/frontend/public/vite.svg b/submissions/T-075_Turinators/code/frontend/public/vite.svg new file mode 100644 index 00000000..e7b8dfb1 --- /dev/null +++ b/submissions/T-075_Turinators/code/frontend/public/vite.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/submissions/T-075_Turinators/code/frontend/src/App.jsx b/submissions/T-075_Turinators/code/frontend/src/App.jsx new file mode 100644 index 00000000..785687a7 --- /dev/null +++ b/submissions/T-075_Turinators/code/frontend/src/App.jsx @@ -0,0 +1,74 @@ +import { useState } from "react" +import { Toast } from "./components/Toast" + +const UploadLogo = () => () +function App() { + const [file, setFile] = useState(null) + const [message, setMessage] = useState(null) + const [output, setOutput] = useState(null) + const [loading, setLoading] = useState(false) + const [toasts, setToasts] = useState([]); + + const showToast = (msg) => { + const id = Date.now(); + const newToast = ( + setToasts((prev) => prev.filter((t) => t.key !== id))} + /> + ); + setToasts((prev) => [...prev, newToast]); + }; + + const handleUpload = async () => { + if (!file) { + showToast(
Please select a file
) + return + } + setLoading(true) + + const formData = new FormData(); + formData.append("video", file); + + const res = await fetch("http://localhost:5000/upload", { + method: "POST", + body: formData, + }); + + const data = await res.json(); + console.log(data) + setOutput(data.url); + setLoading(false); + } + return ( +
+ {toasts.map((toast) => toast)} +
+ {loading ? "loading" : (<> + + setFile(e.target.files?.[0] || null)} /> + + + )} +
+ {file && ( + +
+ +
+ )} + {output && (
+ +
Video successfully processed
+ Download +
+ )} +
+ ) +} + +export default App diff --git a/submissions/T-075_Turinators/code/frontend/src/assets/react.svg b/submissions/T-075_Turinators/code/frontend/src/assets/react.svg new file mode 100644 index 00000000..6c87de9b --- /dev/null +++ b/submissions/T-075_Turinators/code/frontend/src/assets/react.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/submissions/T-075_Turinators/code/frontend/src/components/Navbar.jsx b/submissions/T-075_Turinators/code/frontend/src/components/Navbar.jsx new file mode 100644 index 00000000..2e915fb3 --- /dev/null +++ b/submissions/T-075_Turinators/code/frontend/src/components/Navbar.jsx @@ -0,0 +1,18 @@ +import React from 'react'; + +const HamburgerMenu = () => ( + +) +const GithubLogo = () => () + + +function Navbar(props) { + return ( + + ); +} + +export default Navbar; \ No newline at end of file diff --git a/submissions/T-075_Turinators/code/frontend/src/components/Sidebar.jsx b/submissions/T-075_Turinators/code/frontend/src/components/Sidebar.jsx new file mode 100644 index 00000000..23afe8fe --- /dev/null +++ b/submissions/T-075_Turinators/code/frontend/src/components/Sidebar.jsx @@ -0,0 +1,26 @@ +import React from 'react'; + +const UploadLogo = () => () + +function Sidebar(props) { + return ( +
+
+ + Upload Code + +
+
+ About Us +
+
+ Dataset +
+
+ +
+
+ ); +} + +export default Sidebar; \ No newline at end of file diff --git a/submissions/T-075_Turinators/code/frontend/src/components/Toast.jsx b/submissions/T-075_Turinators/code/frontend/src/components/Toast.jsx new file mode 100644 index 00000000..00da5449 --- /dev/null +++ b/submissions/T-075_Turinators/code/frontend/src/components/Toast.jsx @@ -0,0 +1,24 @@ +import { useEffect, useState } from "react"; + +export const Toast = ({ message, duration = 3000, onClose }) => { + const [visible, setVisible] = useState(true); + + useEffect(() => { + const timer = setTimeout(() => { + setVisible(false); + if (onClose) onClose(); + }, duration); + + return () => clearTimeout(timer); + }, [duration, onClose]); + + if (!visible) return null; + + return ( +
+
+ {message} +
+
+ ); +}; diff --git a/submissions/T-075_Turinators/code/frontend/src/index.css b/submissions/T-075_Turinators/code/frontend/src/index.css new file mode 100644 index 00000000..bd6213e1 --- /dev/null +++ b/submissions/T-075_Turinators/code/frontend/src/index.css @@ -0,0 +1,3 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; \ No newline at end of file diff --git a/submissions/T-075_Turinators/code/frontend/src/main.jsx b/submissions/T-075_Turinators/code/frontend/src/main.jsx new file mode 100644 index 00000000..3501b790 --- /dev/null +++ b/submissions/T-075_Turinators/code/frontend/src/main.jsx @@ -0,0 +1,20 @@ +import { StrictMode } from 'react' +import { createRoot } from 'react-dom/client' +import './index.css' +import App from './App.jsx' +import Navbar from './components/Navbar.jsx' +import Sidebar from './components/Sidebar.jsx' + + +createRoot(document.getElementById('root')).render( + + +
+
+ +
+ +
+ + , +) diff --git a/submissions/T-075_Turinators/code/frontend/tailwind.config.js b/submissions/T-075_Turinators/code/frontend/tailwind.config.js new file mode 100644 index 00000000..89a305e0 --- /dev/null +++ b/submissions/T-075_Turinators/code/frontend/tailwind.config.js @@ -0,0 +1,11 @@ +/** @type {import('tailwindcss').Config} */ +export default { + content: [ + "./index.html", + "./src/**/*.{js,ts,jsx,tsx}", + ], + theme: { + extend: {}, + }, + plugins: [], +} \ No newline at end of file diff --git a/submissions/T-075_Turinators/code/frontend/vite.config.js b/submissions/T-075_Turinators/code/frontend/vite.config.js new file mode 100644 index 00000000..8b0f57b9 --- /dev/null +++ b/submissions/T-075_Turinators/code/frontend/vite.config.js @@ -0,0 +1,7 @@ +import { defineConfig } from 'vite' +import react from '@vitejs/plugin-react' + +// https://vite.dev/config/ +export default defineConfig({ + plugins: [react()], +}) diff --git a/submissions/T-075_Turinators/code/ml/CT_Hacka.ipynb b/submissions/T-075_Turinators/code/ml/CT_Hacka.ipynb new file mode 100644 index 00000000..c0e7e6c1 --- /dev/null +++ b/submissions/T-075_Turinators/code/ml/CT_Hacka.ipynb @@ -0,0 +1,658 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-02T16:45:56.777580Z", + "iopub.status.busy": "2025-10-02T16:45:56.777324Z", + "iopub.status.idle": "2025-10-02T16:47:29.380336Z", + "shell.execute_reply": "2025-10-02T16:47:29.379392Z", + "shell.execute_reply.started": "2025-10-02T16:45:56.777560Z" + }, + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting ultralytics\n", + " Downloading ultralytics-8.3.204-py3-none-any.whl.metadata (37 kB)\n", + "Requirement already satisfied: numpy>=1.23.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (1.26.4)\n", + "Requirement already satisfied: 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nvidia-cudnn-cu12, nvidia-cusolver-cu12, ultralytics-thop, ultralytics\n", + " Attempting uninstall: nvidia-nvjitlink-cu12\n", + " Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n", + " Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n", + " Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n", + " Attempting uninstall: nvidia-curand-cu12\n", + " Found existing installation: nvidia-curand-cu12 10.3.6.82\n", + " Uninstalling nvidia-curand-cu12-10.3.6.82:\n", + " Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n", + " Attempting uninstall: nvidia-cufft-cu12\n", + " Found existing installation: nvidia-cufft-cu12 11.2.3.61\n", + " Uninstalling nvidia-cufft-cu12-11.2.3.61:\n", + " Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n", + " Attempting uninstall: nvidia-cuda-runtime-cu12\n", + " Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n", + " Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n", + " Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n", + " Attempting uninstall: nvidia-cuda-nvrtc-cu12\n", + " Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n", + " Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n", + " Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n", + " Attempting uninstall: nvidia-cuda-cupti-cu12\n", + " Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n", + " Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n", + " Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n", + " Attempting uninstall: nvidia-cublas-cu12\n", + " Found existing installation: nvidia-cublas-cu12 12.5.3.2\n", + " Uninstalling nvidia-cublas-cu12-12.5.3.2:\n", + " Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n", + " Attempting uninstall: nvidia-cusparse-cu12\n", + " Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n", + " Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n", + " Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n", + " Attempting uninstall: nvidia-cudnn-cu12\n", + " Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n", + " Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n", + " Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n", + " Attempting uninstall: nvidia-cusolver-cu12\n", + " Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n", + " Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n", + " Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n", + "Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 ultralytics-8.3.204 ultralytics-thop-2.0.17\n" + ] + } + ], + "source": [ + "!pip install ultralytics" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-02T16:48:26.474934Z", + "iopub.status.busy": "2025-10-02T16:48:26.474621Z", + "iopub.status.idle": "2025-10-02T16:48:33.408243Z", + "shell.execute_reply": "2025-10-02T16:48:33.407569Z", + "shell.execute_reply.started": "2025-10-02T16:48:26.474896Z" + }, + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating new Ultralytics Settings v0.0.6 file ✅ \n", + "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n", + "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n", + "\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n.pt to 'yolov8n.pt': 100% ━━━━━━━━━━━━ 6.2MB 12.8MB/s 0.5s.4s<0.1s\n" + ] + } + ], + "source": [ + "from ultralytics import YOLO\n", + "\n", + "# Nano model use karte hain (fast aur lightweight)\n", + "model = YOLO(\"yolov8n.pt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-02T16:53:24.011791Z", + "iopub.status.busy": "2025-10-02T16:53:24.011482Z", + "iopub.status.idle": "2025-10-02T17:26:23.004172Z", + "shell.execute_reply": "2025-10-02T17:26:23.003247Z", + "shell.execute_reply.started": "2025-10-02T16:53:24.011772Z" + }, + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ultralytics 8.3.204 🚀 Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (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, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/kaggle/input/radroad-anomaly-detection/images/data.yaml, degrees=0.0, deterministic=True, device=None, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=20, 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=/kaggle/working/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\n", + "\u001b[KDownloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf': 100% ━━━━━━━━━━━━ 755.1KB 16.0MB/s 0.0s\n", + "Overriding model.yaml nc=80 with nc=6\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 752482 ultralytics.nn.modules.head.Detect [6, [64, 128, 256]] \n", + "Model summary: 129 layers, 3,012,018 parameters, 3,012,002 gradients, 8.2 GFLOPs\n", + "\n", + "Transferred 319/355 items from pretrained weights\n", + "Freezing layer 'model.22.dfl.conv.weight'\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n", + "\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt': 100% ━━━━━━━━━━━━ 5.4MB 60.2MB/s 0.1s\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.6±0.6 ms, read: 24.7±11.1 MB/s, size: 280.9 KB)\n", + "\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/input/radroad-anomaly-detection/images/train/labels... 5843 images, 64 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 5843/5843 201.6it/s 29.0s<0.1s\n", + "WARNING ⚠️ \u001b[34m\u001b[1mtrain: \u001b[0mCache directory /kaggle/input/radroad-anomaly-detection/images/train is not writeable, cache not saved.\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[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 28.9±12.6 MB/s, size: 274.7 KB)\n", + "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/input/radroad-anomaly-detection/images/valid/labels... 1288 images, 14 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 1288/1288 197.1it/s 6.5s<0.0s\n", + "WARNING ⚠️ \u001b[34m\u001b[1mval: \u001b[0mCache directory /kaggle/input/radroad-anomaly-detection/images/valid is not writeable, cache not saved.\n", + "Plotting labels to /kaggle/working/runs/detect/train4/labels.jpg... \n", + "\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=0.001, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\n", + "Image sizes 640 train, 640 val\n", + "Using 2 dataloader workers\n", + "Logging results to \u001b[1m/kaggle/working/runs/detect/train4\u001b[0m\n", + "Starting training for 20 epochs...\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 1/20 2.14G 1.763 2.169 1.302 14 640: 100% ━━━━━━━━━━━━ 366/366 4.0it/s 1:32<0.2ss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 3.8it/s 10.8s0.3s\n", + " all 1288 4559 0.664 0.393 0.417 0.204\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 2/20 2.26G 1.721 1.596 1.309 24 640: 100% ━━━━━━━━━━━━ 366/366 4.2it/s 1:27<0.2ss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.3it/s 9.5s0.2s\n", + " all 1288 4559 0.528 0.41 0.4 0.201\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 3/20 2.28G 1.71 1.473 1.312 23 640: 100% ━━━━━━━━━━━━ 366/366 4.3it/s 1:26<0.2ss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.4it/s 9.3s0.2s\n", + " all 1288 4559 0.515 0.457 0.468 0.238\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 4/20 2.29G 1.675 1.362 1.303 11 640: 100% ━━━━━━━━━━━━ 366/366 4.3it/s 1:250.4sss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.4it/s 9.3s0.2s\n", + " all 1288 4559 0.648 0.413 0.466 0.23\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 5/20 2.32G 1.644 1.292 1.29 18 640: 100% ━━━━━━━━━━━━ 366/366 4.3it/s 1:260.4sss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.6it/s 8.9s0.2s\n", + " all 1288 4559 0.511 0.52 0.518 0.261\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 6/20 2.32G 1.616 1.223 1.272 21 640: 100% ━━━━━━━━━━━━ 366/366 4.3it/s 1:260.4sss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.6it/s 9.0s0.2s\n", + " all 1288 4559 0.507 0.545 0.53 0.272\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 7/20 2.35G 1.596 1.18 1.26 17 640: 100% ━━━━━━━━━━━━ 366/366 4.3it/s 1:260.4sss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.7it/s 8.6s0.2s\n", + " all 1288 4559 0.636 0.564 0.576 0.283\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 8/20 2.36G 1.583 1.14 1.252 19 640: 100% ━━━━━━━━━━━━ 366/366 4.4it/s 1:240.4sss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.8it/s 8.6s0.2s\n", + " all 1288 4559 0.598 0.583 0.593 0.306\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 9/20 2.38G 1.551 1.104 1.235 15 640: 100% ━━━━━━━━━━━━ 366/366 4.4it/s 1:230.4sss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.6it/s 9.0s0.2s\n", + " all 1288 4559 0.619 0.615 0.611 0.316\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 10/20 2.39G 1.536 1.074 1.233 16 640: 100% ━━━━━━━━━━━━ 366/366 4.4it/s 1:220.4sss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.6it/s 9.0s0.2s\n", + " all 1288 4559 0.632 0.661 0.642 0.331\n", + "Closing 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", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 11/20 2.42G 1.531 1.032 1.249 11 640: 100% ━━━━━━━━━━━━ 366/366 4.4it/s 1:240.4sss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.7it/s 8.8s0.2s\n", + " all 1288 4559 0.693 0.629 0.656 0.347\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 12/20 2.43G 1.508 0.9879 1.24 10 640: 100% ━━━━━━━━━━━━ 366/366 4.4it/s 1:230.4sss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.4it/s 9.3s0.2s\n", + " all 1288 4559 0.705 0.625 0.659 0.35\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 13/20 2.45G 1.496 0.952 1.227 8 640: 100% ━━━━━━━━━━━━ 366/366 4.3it/s 1:250.4sss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.4it/s 9.3s0.2s\n", + " all 1288 4559 0.735 0.627 0.679 0.356\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 14/20 2.46G 1.472 0.936 1.213 13 640: 100% ━━━━━━━━━━━━ 366/366 4.3it/s 1:260.4sss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.4it/s 9.3s0.2s\n", + " all 1288 4559 0.715 0.662 0.691 0.369\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 15/20 2.49G 1.458 0.9035 1.21 14 640: 100% ━━━━━━━━━━━━ 366/366 4.4it/s 1:240.4sss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.6it/s 9.0s0.2s\n", + " all 1288 4559 0.758 0.669 0.71 0.386\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 16/20 2.49G 1.437 0.8853 1.199 8 640: 100% ━━━━━━━━━━━━ 366/366 4.4it/s 1:230.4sss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.6it/s 8.9s0.2s\n", + " all 1288 4559 0.765 0.672 0.716 0.392\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 17/20 2.52G 1.418 0.8564 1.188 4 640: 100% ━━━━━━━━━━━━ 366/366 4.4it/s 1:23<0.2ss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.6it/s 8.9s0.2s\n", + " all 1288 4559 0.785 0.693 0.739 0.408\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 18/20 2.53G 1.407 0.834 1.181 4 640: 100% ━━━━━━━━━━━━ 366/366 4.4it/s 1:240.4sss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.5it/s 9.2s0.2s\n", + " all 1288 4559 0.756 0.7 0.741 0.413\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 19/20 2.55G 1.385 0.8108 1.164 17 640: 100% ━━━━━━━━━━━━ 366/366 4.5it/s 1:220.4sss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.7it/s 8.6s0.2s\n", + " all 1288 4559 0.792 0.692 0.757 0.419\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 20/20 2.56G 1.37 0.7948 1.158 9 640: 100% ━━━━━━━━━━━━ 366/366 4.4it/s 1:230.4sss\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.5it/s 9.1s0.2s\n", + " all 1288 4559 0.785 0.716 0.761 0.423\n", + "\n", + "20 epochs completed in 0.525 hours.\n", + "Optimizer stripped from /kaggle/working/runs/detect/train4/weights/last.pt, 6.2MB\n", + "Optimizer stripped from /kaggle/working/runs/detect/train4/weights/best.pt, 6.2MB\n", + "\n", + "Validating /kaggle/working/runs/detect/train4/weights/best.pt...\n", + "Ultralytics 8.3.204 🚀 Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (Tesla T4, 15095MiB)\n", + "Model summary (fused): 72 layers, 3,006,818 parameters, 0 gradients, 8.1 GFLOPs\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 41/41 4.2it/s 9.8s0.2s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 1288 4559 0.784 0.716 0.76 0.423\n", + " HMV 511 672 0.916 0.957 0.969 0.702\n", + " LMV 960 2201 0.873 0.925 0.947 0.599\n", + " Pedestrian 337 549 0.796 0.792 0.845 0.433\n", + " RoadDamages 474 964 0.693 0.364 0.484 0.183\n", + " SpeedBump 74 83 0.628 0.735 0.723 0.336\n", + " UnsurfacedRoad 83 90 0.795 0.522 0.594 0.285\n", + "Speed: 0.1ms preprocess, 1.4ms inference, 0.0ms loss, 1.3ms postprocess per image\n", + "Results saved to \u001b[1m/kaggle/working/runs/detect/train4\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "ultralytics.utils.metrics.DetMetrics object with attributes:\n", + "\n", + "ap_class_index: array([0, 1, 2, 3, 4, 5])\n", + "box: ultralytics.utils.metrics.Metric object\n", + "confusion_matrix: \n", + "curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']\n", + "curves_results: [[array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,\n", + " 0.024024, 0.025025, 0.026026, 0.027027, 0.028028, 0.029029, 0.03003, 0.031031, 0.032032, 0.033033, 0.034034, 0.035035, 0.036036, 0.037037, 0.038038, 0.039039, 0.04004, 0.041041, 0.042042, 0.043043, 0.044044, 0.045045, 0.046046, 0.047047,\n", + " 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0.78679, 0.78779, 0.78879, 0.78979, 0.79079, 0.79179,\n", + " 0.79279, 0.79379, 0.79479, 0.7958, 0.7968, 0.7978, 0.7988, 0.7998, 0.8008, 0.8018, 0.8028, 0.8038, 0.8048, 0.80581, 0.80681, 0.80781, 0.80881, 0.80981, 0.81081, 0.81181, 0.81281, 0.81381, 0.81481, 0.81582,\n", + " 0.81682, 0.81782, 0.81882, 0.81982, 0.82082, 0.82182, 0.82282, 0.82382, 0.82482, 0.82583, 0.82683, 0.82783, 0.82883, 0.82983, 0.83083, 0.83183, 0.83283, 0.83383, 0.83483, 0.83584, 0.83684, 0.83784, 0.83884, 0.83984,\n", + " 0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.84985, 0.85085, 0.85185, 0.85285, 0.85385, 0.85485, 0.85586, 0.85686, 0.85786, 0.85886, 0.85986, 0.86086, 0.86186, 0.86286, 0.86386,\n", + " 0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,\n", + " 0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,\n", + " 0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,\n", + " 0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,\n", + " 0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,\n", + " 0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 0.99107, 0.99107, 0.98958, ..., 0, 0, 0],\n", + " [ 0.98955, 0.98955, 0.9891, ..., 0, 0, 0],\n", + " [ 0.96903, 0.96903, 0.96721, ..., 0, 0, 0],\n", + " [ 0.9388, 0.9388, 0.9305, ..., 0, 0, 0],\n", + " [ 0.96386, 0.96386, 0.95181, ..., 0, 0, 0],\n", + " [ 0.84444, 0.84444, 0.82222, ..., 0, 0, 0]]), 'Confidence', 'Recall']]\n", + "fitness: 0.42307425666931875\n", + "keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']\n", + "maps: array([ 0.70194, 0.5995, 0.43309, 0.18277, 0.33615, 0.28499])\n", + "names: {0: 'HMV', 1: 'LMV', 2: 'Pedestrian', 3: 'RoadDamages', 4: 'SpeedBump', 5: 'UnsurfacedRoad'}\n", + "nt_per_class: array([ 672, 2201, 549, 964, 83, 90])\n", + "nt_per_image: array([511, 960, 337, 474, 74, 83])\n", + "results_dict: {'metrics/precision(B)': 0.7835103748181039, 'metrics/recall(B)': 0.7158352578833607, 'metrics/mAP50(B)': 0.7604126118560929, 'metrics/mAP50-95(B)': 0.42307425666931875, 'fitness': 0.42307425666931875}\n", + "save_dir: PosixPath('/kaggle/working/runs/detect/train4')\n", + "speed: {'preprocess': 0.12370382142883993, 'inference': 1.4295330954970236, 'loss': 0.00043667624406157725, 'postprocess': 1.2698994231355354}\n", + "stats: {'tp': [], 'conf': [], 'pred_cls': [], 'target_cls': [], 'target_img': []}\n", + "task: 'detect'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.train(\n", + " data=\"/kaggle/input/radroad-anomaly-detection/images/data.yaml\", \n", + " epochs=20, \n", + " imgsz=640\n", + ")" + ] + } + ], + "metadata": { + "kaggle": { + "accelerator": "nvidiaTeslaT4", + "dataSources": [ + { + "datasetId": 4006021, + "sourceId": 7074449, + "sourceType": "datasetVersion" + } + ], + "dockerImageVersionId": 31089, + "isGpuEnabled": true, + "isInternetEnabled": true, + "language": "python", + "sourceType": "notebook" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/submissions/T-075_Turinators/code/ml/mock-run.ipynb b/submissions/T-075_Turinators/code/ml/mock-run.ipynb new file mode 100644 index 00000000..da77cdbf --- /dev/null +++ b/submissions/T-075_Turinators/code/ml/mock-run.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":7074449,"sourceType":"datasetVersion","datasetId":4006021}],"dockerImageVersionId":31090,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install ultralytics","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-10-03T11:05:02.693861Z","iopub.execute_input":"2025-10-03T11:05:02.694068Z","iopub.status.idle":"2025-10-03T11:05:05.860132Z","shell.execute_reply.started":"2025-10-03T11:05:02.694045Z","shell.execute_reply":"2025-10-03T11:05:05.859197Z"}},"outputs":[{"name":"stdout","text":"Requirement already satisfied: ultralytics in /usr/local/lib/python3.11/dist-packages (8.3.204)\nRequirement already satisfied: numpy>=1.23.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (1.26.4)\nRequirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (3.7.2)\nRequirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (4.11.0.86)\nRequirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (11.2.1)\nRequirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (6.0.2)\nRequirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.32.4)\nRequirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (1.15.3)\nRequirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.6.0+cu124)\nRequirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (0.21.0+cu124)\nRequirement already satisfied: psutil in /usr/local/lib/python3.11/dist-packages (from ultralytics) (7.0.0)\nRequirement already satisfied: polars in /usr/local/lib/python3.11/dist-packages (from ultralytics) (1.21.0)\nRequirement already satisfied: ultralytics-thop>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.0.17)\nRequirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.3.2)\nRequirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\nRequirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.58.4)\nRequirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.4.8)\nRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (25.0)\nRequirement already satisfied: pyparsing<3.1,>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.0.9)\nRequirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (2.9.0.post0)\nRequirement already satisfied: mkl_fft in /usr/local/lib/python3.11/dist-packages (from numpy>=1.23.0->ultralytics) (1.3.8)\nRequirement already satisfied: mkl_random in /usr/local/lib/python3.11/dist-packages (from numpy>=1.23.0->ultralytics) (1.2.4)\nRequirement already satisfied: mkl_umath in /usr/local/lib/python3.11/dist-packages (from numpy>=1.23.0->ultralytics) (0.1.1)\nRequirement already satisfied: mkl in /usr/local/lib/python3.11/dist-packages (from numpy>=1.23.0->ultralytics) (2025.2.0)\nRequirement already satisfied: tbb4py in /usr/local/lib/python3.11/dist-packages (from numpy>=1.23.0->ultralytics) (2022.2.0)\nRequirement already satisfied: mkl-service in /usr/local/lib/python3.11/dist-packages (from numpy>=1.23.0->ultralytics) (2.4.1)\nRequirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (3.4.2)\nRequirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (3.10)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (2.5.0)\nRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (2025.6.15)\nRequirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.18.0)\nRequirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (4.14.0)\nRequirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.5)\nRequirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.1.6)\nRequirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (2025.5.1)\nRequirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (12.4.127)\nRequirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (12.4.127)\nRequirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (12.4.127)\nRequirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (9.1.0.70)\nRequirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (12.4.5.8)\nRequirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (11.2.1.3)\nRequirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (10.3.5.147)\nRequirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (11.6.1.9)\nRequirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (12.3.1.170)\nRequirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (0.6.2)\nRequirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (2.21.5)\nRequirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (12.4.127)\nRequirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (12.4.127)\nRequirement already satisfied: triton==3.2.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.2.0)\nRequirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (1.13.1)\nRequirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch>=1.8.0->ultralytics) (1.3.0)\nRequirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.17.0)\nRequirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (3.0.2)\nRequirement already satisfied: intel-openmp<2026,>=2024 in /usr/local/lib/python3.11/dist-packages (from mkl->numpy>=1.23.0->ultralytics) (2024.2.0)\nRequirement already satisfied: tbb==2022.* in /usr/local/lib/python3.11/dist-packages (from mkl->numpy>=1.23.0->ultralytics) (2022.2.0)\nRequirement already satisfied: tcmlib==1.* in /usr/local/lib/python3.11/dist-packages (from tbb==2022.*->mkl->numpy>=1.23.0->ultralytics) (1.4.0)\nRequirement already satisfied: intel-cmplr-lib-rt in /usr/local/lib/python3.11/dist-packages (from mkl_umath->numpy>=1.23.0->ultralytics) (2024.2.0)\nRequirement already satisfied: intel-cmplr-lib-ur==2024.2.0 in /usr/local/lib/python3.11/dist-packages (from intel-openmp<2026,>=2024->mkl->numpy>=1.23.0->ultralytics) (2024.2.0)\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"import os\nprint(os.listdir(\"/kaggle/input\")) # watch for 'radroad-anomaly-detection'\nprint(os.listdir(\"/kaggle/input/radroad-anomaly-detection\"))\nprint(os.listdir(\"/kaggle/input/radroad-anomaly-detection/images\"))\nprint(os.listdir(\"/kaggle/input/radroad-anomaly-detection/images/test\")) # should show ['images', 'labels']\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-03T11:05:37.423366Z","iopub.execute_input":"2025-10-03T11:05:37.423638Z","iopub.status.idle":"2025-10-03T11:05:37.439470Z","shell.execute_reply.started":"2025-10-03T11:05:37.423607Z","shell.execute_reply":"2025-10-03T11:05:37.438880Z"}},"outputs":[{"name":"stdout","text":"['radroad-anomaly-detection']\n['images', 'videos_without_audio']\n['data.yaml', 'valid', 'test', 'train']\n['labels', 'images']\n","output_type":"stream"}],"execution_count":3},{"cell_type":"code","source":"import os\nfrom ultralytics import YOLO\n\n# Adjust only if Step 1 shows different names\nORIG_IMAGES_DIR = \"/kaggle/input/radroad-anomaly-detection/images/test/images\"\nORIG_LABELS_DIR = \"/kaggle/input/radroad-anomaly-detection/images/test/labels\"\n\n# Output/working dirs\nWORK_IMG_DIR = \"/kaggle/working/images/test\"\nWORK_LBL_DIR = \"/kaggle/working/labels/test\"\nFILTER_TMP_DIR = \"/kaggle/working/labels/test_damages_only\"\n\nORIGINAL_CLASS_ID = 2 # 'Road Damages' in original dataset → will remap to 0\n\n# Guards\nfor p in [ORIG_IMAGES_DIR, ORIG_LABELS_DIR]:\n if not os.path.isdir(p):\n raise FileNotFoundError(f\"Missing: {p}\")\n\nos.makedirs(FILTER_TMP_DIR, exist_ok=True)\nos.makedirs(WORK_IMG_DIR, exist_ok=True)\nos.makedirs(WORK_LBL_DIR, exist_ok=True)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-03T11:05:55.388094Z","iopub.execute_input":"2025-10-03T11:05:55.388657Z","iopub.status.idle":"2025-10-03T11:06:00.201702Z","shell.execute_reply.started":"2025-10-03T11:05:55.388635Z","shell.execute_reply":"2025-10-03T11:06:00.200926Z"}},"outputs":[{"name":"stdout","text":"Creating new Ultralytics Settings v0.0.6 file ✅ \nView Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\nUpdate Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"def filter_labels(original_dir, new_dir, orig_cls=ORIGINAL_CLASS_ID):\n for filename in os.listdir(original_dir):\n src = os.path.join(original_dir, filename)\n if not os.path.isfile(src):\n continue\n new_lines = []\n with open(src, \"r\") as f:\n for line in f:\n line = line.strip()\n if not line:\n continue\n if line.startswith(str(orig_cls) + \" \"):\n new_lines.append(\"0\" + line[len(str(orig_cls)):])\n if new_lines:\n with open(os.path.join(new_dir, filename), \"w\") as out:\n out.writelines(l + \"\\n\" if not l.endswith(\"\\n\") else l for l in new_lines)\n\nprint(\"Filtering labels...\")\nfilter_labels(ORIG_LABELS_DIR, FILTER_TMP_DIR)\nprint(\"Done filtering.\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-03T11:06:28.640677Z","iopub.execute_input":"2025-10-03T11:06:28.641354Z","iopub.status.idle":"2025-10-03T11:06:33.756415Z","shell.execute_reply.started":"2025-10-03T11:06:28.641329Z","shell.execute_reply":"2025-10-03T11:06:33.755813Z"}},"outputs":[{"name":"stdout","text":"Filtering labels...\nDone filtering.\n","output_type":"stream"}],"execution_count":5},{"cell_type":"code","source":"# Link/copy images to working images folder\nfor fname in os.listdir(ORIG_IMAGES_DIR):\n s = os.path.join(ORIG_IMAGES_DIR, fname)\n d = os.path.join(WORK_IMG_DIR, fname)\n if os.path.isfile(s) and not os.path.exists(d):\n try:\n os.symlink(s, d) # fast; same-session only\n except OSError:\n import shutil\n shutil.copy2(s, d)\n\n# Move filtered labels into working labels folder\nfor fname in os.listdir(FILTER_TMP_DIR):\n s = os.path.join(FILTER_TMP_DIR, fname)\n d = os.path.join(WORK_LBL_DIR, fname)\n if os.path.isfile(s):\n os.replace(s, d)\n\n# Quick sanity: count paired samples\nimg_basenames = {os.path.splitext(f)[0] for f in os.listdir(WORK_IMG_DIR) if os.path.isfile(os.path.join(WORK_IMG_DIR, f))}\nlbl_basenames = {os.path.splitext(f)[0] for f in os.listdir(WORK_LBL_DIR) if os.path.isfile(os.path.join(WORK_LBL_DIR, f))}\npaired = len(img_basenames & lbl_basenames)\nprint(\"Images:\", len(img_basenames), \"Labels:\", len(lbl_basenames), \"Paired:\", paired)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-03T11:06:54.875933Z","iopub.execute_input":"2025-10-03T11:06:54.876496Z","iopub.status.idle":"2025-10-03T11:06:57.781943Z","shell.execute_reply.started":"2025-10-03T11:06:54.876471Z","shell.execute_reply":"2025-10-03T11:06:57.781348Z"}},"outputs":[{"name":"stdout","text":"Images: 1263 Labels: 317 Paired: 317\n","output_type":"stream"}],"execution_count":6},{"cell_type":"code","source":"%%writefile road_damages_only.yaml\ntrain: /kaggle/working/images/test\nval: /kaggle/working/images/test\n\nnc: 1\nnames:\n 0: Road Damages\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-03T11:07:08.986754Z","iopub.execute_input":"2025-10-03T11:07:08.987300Z","iopub.status.idle":"2025-10-03T11:07:08.991962Z","shell.execute_reply.started":"2025-10-03T11:07:08.987277Z","shell.execute_reply":"2025-10-03T11:07:08.991437Z"}},"outputs":[{"name":"stdout","text":"Writing road_damages_only.yaml\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"model = YOLO(\"yolov8l.pt\")\nprint(\"Starting training...\")\nresults = model.train(\n data=\"road_damages_only.yaml\",\n epochs=50,\n imgsz=640,\n project=\"road_damage_detection\",\n name=\"run_damages_only\"\n)\nprint(\"Training finished!\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-03T11:07:27.861761Z","iopub.execute_input":"2025-10-03T11:07:27.862287Z","iopub.status.idle":"2025-10-03T12:20:10.458624Z","shell.execute_reply.started":"2025-10-03T11:07:27.862263Z","shell.execute_reply":"2025-10-03T12:20:10.457737Z"}},"outputs":[{"name":"stdout","text":"\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l.pt to 'yolov8l.pt': 100% ━━━━━━━━━━━━ 83.7MB 173.3MB/s 0.5s0.4s<0.1s\nStarting training...\nUltralytics 8.3.204 🚀 Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (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, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=road_damages_only.yaml, degrees=0.0, deterministic=True, device=None, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=50, 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=yolov8l.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=run_damages_only, 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=road_damage_detection, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=/kaggle/working/road_damage_detection/run_damages_only, 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\n\u001b[KDownloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf': 100% ━━━━━━━━━━━━ 755.1KB 16.5MB/s 0.0s\nOverriding model.yaml nc=80 with nc=1\n\n from n params module arguments \n 0 -1 1 1856 ultralytics.nn.modules.conv.Conv [3, 64, 3, 2] \n 1 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] \n 2 -1 3 279808 ultralytics.nn.modules.block.C2f [128, 128, 3, True] \n 3 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] \n 4 -1 6 2101248 ultralytics.nn.modules.block.C2f [256, 256, 6, True] \n 5 -1 1 1180672 ultralytics.nn.modules.conv.Conv [256, 512, 3, 2] \n 6 -1 6 8396800 ultralytics.nn.modules.block.C2f [512, 512, 6, True] \n 7 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] \n 8 -1 3 4461568 ultralytics.nn.modules.block.C2f [512, 512, 3, True] \n 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 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 3 4723712 ultralytics.nn.modules.block.C2f [1024, 512, 3] \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 3 1247744 ultralytics.nn.modules.block.C2f [768, 256, 3] \n 16 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] \n 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n 18 -1 3 4592640 ultralytics.nn.modules.block.C2f [768, 512, 3] \n 19 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] \n 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n 21 -1 3 4723712 ultralytics.nn.modules.block.C2f [1024, 512, 3] \n 22 [15, 18, 21] 1 5583571 ultralytics.nn.modules.head.Detect [1, [256, 512, 512]] \nModel summary: 209 layers, 43,630,611 parameters, 43,630,595 gradients, 165.4 GFLOPs\n\nTransferred 589/595 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[KDownloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt': 100% ━━━━━━━━━━━━ 5.4MB 67.6MB/s 0.1s\n\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 29.2±5.7 MB/s, size: 256.3 KB)\n\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/working/labels/test... 317 images, 946 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 1263/1263 294.1it/s 4.3s0.0s\n\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /kaggle/working/labels/test.cache\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[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 385.8±96.5 MB/s, size: 259.7 KB)\n\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/labels/test.cache... 317 images, 946 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 1263/1263 1.7Mit/s 0.0ss\nPlotting labels to /kaggle/working/road_damage_detection/run_damages_only/labels.jpg... \n\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=0.002, momentum=0.9) with parameter groups 97 weight(decay=0.0), 104 weight(decay=0.0005), 103 bias(decay=0.0)\nImage sizes 640 train, 640 val\nUsing 2 dataloader workers\nLogging results to \u001b[1m/kaggle/working/road_damage_detection/run_damages_only\u001b[0m\nStarting training for 50 epochs...\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 1/50 9.23G 2.106 3.685 1.471 9 640: 100% ━━━━━━━━━━━━ 79/79 1.3it/s 1:010.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 5% ╸─────────── 2/40 1.0it/s 1.5s<39.2s","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/ultralytics/engine/validator.py:288: RuntimeWarning: invalid value encountered in greater_equal\n matches = np.nonzero(iou >= threshold) # IoU > threshold and classes match\n","output_type":"stream"},{"name":"stdout","text":"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.3s0.4s\n all 1263 561 0.00158 0.517 0.00111 0.000493\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 2/50 10G 2.358 2.696 1.634 9 640: 100% ━━━━━━━━━━━━ 79/79 1.1it/s 1:090.9s3s\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 10% ━─────────── 4/40 1.9it/s 1.6s<18.9s","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/ultralytics/engine/validator.py:288: RuntimeWarning: invalid value encountered in greater_equal\n matches = np.nonzero(iou >= threshold) # IoU > threshold and classes match\n","output_type":"stream"},{"name":"stdout","text":"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 15.8s0.4s\n all 1263 561 0 0 0 0\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 3/50 10G 2.366 2.999 1.682 7 640: 100% ━━━━━━━━━━━━ 79/79 1.1it/s 1:100.9sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.3s0.4s\n all 1263 561 0.276 0.194 0.14 0.0556\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 4/50 9.98G 2.305 2.803 1.637 8 640: 100% ━━━━━━━━━━━━ 79/79 1.1it/s 1:100.9sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.5s0.4s\n all 1263 561 0.0207 0.0553 0.0102 0.00376\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 5/50 9.78G 2.148 2.455 1.532 14 640: 100% ━━━━━━━━━━━━ 79/79 1.1it/s 1:090.9s6s\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.2s0.4s\n all 1263 561 0.421 0.266 0.251 0.102\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 6/50 9.66G 2.262 2.536 1.609 2 640: 100% ━━━━━━━━━━━━ 79/79 1.1it/s 1:090.9s4s\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.3s0.4s\n all 1263 561 0.507 0.374 0.38 0.16\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 7/50 9.61G 2.149 2.307 1.538 6 640: 100% ━━━━━━━━━━━━ 79/79 1.1it/s 1:090.9s4s\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.4s0.4s\n all 1263 561 0.389 0.289 0.267 0.105\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 8/50 10G 2.109 2.226 1.497 7 640: 100% ━━━━━━━━━━━━ 79/79 1.1it/s 1:090.8s4s\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.3s0.4s\n all 1263 561 0.454 0.365 0.333 0.139\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 9/50 9.78G 2.088 2.258 1.518 14 640: 100% ━━━━━━━━━━━━ 79/79 1.1it/s 1:090.8s6s\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.5s0.4s\n all 1263 561 0.56 0.44 0.465 0.208\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 10/50 10G 2.072 1.935 1.437 4 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:090.8s4s\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.3s0.4s\n all 1263 561 0.531 0.494 0.469 0.195\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 11/50 9.95G 2.019 2.158 1.496 10 640: 100% ━━━━━━━━━━━━ 79/79 1.1it/s 1:090.9s5s\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.4s0.4s\n all 1263 561 0.514 0.494 0.506 0.214\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 12/50 10.1G 2.01 2.075 1.428 10 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:090.9sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.5s0.4s\n all 1263 561 0.645 0.538 0.563 0.248\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 13/50 10.1G 2.009 1.913 1.417 7 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:090.9sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.3s0.4s\n all 1263 561 0.495 0.451 0.457 0.193\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 14/50 10G 1.999 1.812 1.401 12 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.4s0.4s\n all 1263 561 0.5 0.447 0.465 0.205\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 15/50 9.92G 1.934 1.75 1.373 14 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.4s0.4s\n all 1263 561 0.667 0.531 0.596 0.267\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 16/50 9.91G 1.909 1.663 1.351 9 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.9sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.5s0.4s\n all 1263 561 0.663 0.594 0.632 0.287\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 17/50 9.78G 1.864 1.82 1.355 8 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.3s0.4s\n all 1263 561 0.627 0.561 0.619 0.274\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 18/50 10G 1.835 1.691 1.355 15 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.4s0.4s\n all 1263 561 0.625 0.59 0.636 0.286\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 19/50 9.92G 1.84 1.551 1.35 16 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.4s0.4s\n all 1263 561 0.693 0.554 0.643 0.274\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 20/50 10G 1.851 1.823 1.345 10 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:090.9sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.3s0.4s\n all 1263 561 0.69 0.601 0.658 0.31\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 21/50 9.79G 1.815 1.542 1.298 6 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.3s0.4s\n all 1263 561 0.647 0.647 0.669 0.315\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 22/50 10G 1.882 1.558 1.329 7 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:090.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.4s0.4s\n all 1263 561 0.735 0.698 0.734 0.348\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 23/50 9.93G 1.797 1.516 1.344 11 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.4s0.4s\n all 1263 561 0.657 0.528 0.569 0.259\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 24/50 10.1G 1.834 1.523 1.346 5 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:090.9sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.3s0.4s\n all 1263 561 0.661 0.59 0.626 0.286\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 25/50 9.78G 1.829 1.528 1.296 19 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:090.8s5s\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.4s0.4s\n all 1263 561 0.692 0.702 0.734 0.358\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 26/50 9.93G 1.812 1.548 1.358 8 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:090.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.3s0.4s\n all 1263 561 0.666 0.634 0.665 0.315\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 27/50 10G 1.797 1.547 1.304 1 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.3s0.4s\n all 1263 561 0.664 0.676 0.71 0.338\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 28/50 9.97G 1.784 1.505 1.279 5 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.9sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.4s0.4s\n all 1263 561 0.678 0.722 0.745 0.356\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 29/50 9.79G 1.823 1.482 1.328 5 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.3s0.4s\n all 1263 561 0.71 0.668 0.728 0.354\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 30/50 9.97G 1.706 1.414 1.255 18 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.3s0.4s\n all 1263 561 0.719 0.701 0.741 0.364\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 31/50 9.96G 1.745 1.536 1.292 9 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.1s0.4s\n all 1263 561 0.738 0.601 0.687 0.335\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 32/50 9.94G 1.72 1.406 1.246 7 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:090.9s4s\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.1s0.4s\n all 1263 561 0.758 0.67 0.747 0.391\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 33/50 9.78G 1.727 1.353 1.295 11 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:090.9sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.5s0.4s\n all 1263 561 0.753 0.733 0.784 0.396\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 34/50 9.66G 1.666 1.344 1.254 7 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:090.9sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.4s0.4s\n all 1263 561 0.775 0.724 0.778 0.388\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 35/50 9.72G 1.724 1.281 1.288 19 640: 100% ━━━━━━━━━━━━ 79/79 1.1it/s 1:090.9s4s\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.3s0.4s\n all 1263 561 0.752 0.742 0.797 0.416\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 36/50 10.1G 1.689 1.265 1.26 19 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:090.9sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.3s0.4s\n all 1263 561 0.778 0.768 0.824 0.438\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 37/50 9.92G 1.642 1.218 1.246 10 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:090.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.3s0.4s\n all 1263 561 0.795 0.756 0.815 0.422\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 38/50 9.64G 1.656 1.276 1.256 9 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.4s0.4s\n all 1263 561 0.802 0.807 0.846 0.446\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 39/50 9.71G 1.633 1.171 1.22 9 640: 100% ━━━━━━━━━━━━ 79/79 1.1it/s 1:090.8s5s\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.2s0.4s\n all 1263 561 0.796 0.77 0.824 0.438\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 40/50 10.1G 1.589 1.119 1.194 13 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.9sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.3s0.4s\n all 1263 561 0.749 0.792 0.825 0.452\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\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 41/50 9.79G 1.625 1.261 1.29 5 640: 100% ━━━━━━━━━━━━ 79/79 1.1it/s 1:090.8s4s\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.1s0.4s\n all 1263 561 0.833 0.793 0.848 0.456\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 42/50 9.73G 1.594 1.28 1.233 3 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.1s0.4s\n all 1263 561 0.824 0.736 0.817 0.425\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 43/50 9.72G 1.536 1.205 1.198 4 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.2s0.4s\n all 1263 561 0.795 0.782 0.833 0.447\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 44/50 9.75G 1.581 1.119 1.207 14 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.1s0.4s\n all 1263 561 0.839 0.8 0.878 0.487\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 45/50 9.78G 1.471 1.113 1.168 6 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.4it/s 16.4s0.4s\n all 1263 561 0.789 0.75 0.814 0.428\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 46/50 10G 1.523 1.052 1.216 5 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.3s0.4s\n all 1263 561 0.829 0.805 0.869 0.478\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 47/50 9.91G 1.445 1.051 1.189 9 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.1s0.4s\n all 1263 561 0.872 0.845 0.907 0.52\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 48/50 9.96G 1.468 0.9572 1.188 3 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.0s0.4s\n all 1263 561 0.884 0.811 0.899 0.519\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 49/50 9.88G 1.474 1.048 1.168 5 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.2s0.4s\n all 1263 561 0.87 0.813 0.899 0.513\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[K 50/50 10G 1.426 1.011 1.148 4 640: 100% ━━━━━━━━━━━━ 79/79 1.2it/s 1:080.8sss\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.2s0.4s\n all 1263 561 0.873 0.838 0.909 0.533\n\n50 epochs completed in 1.200 hours.\nOptimizer stripped from /kaggle/working/road_damage_detection/run_damages_only/weights/last.pt, 87.6MB\nOptimizer stripped from /kaggle/working/road_damage_detection/run_damages_only/weights/best.pt, 87.6MB\n\nValidating /kaggle/working/road_damage_detection/run_damages_only/weights/best.pt...\nUltralytics 8.3.204 🚀 Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (Tesla T4, 15095MiB)\nModel summary (fused): 112 layers, 43,607,379 parameters, 0 gradients, 164.8 GFLOPs\n\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 40/40 2.5it/s 16.3s0.4s\n","output_type":"stream"},{"name":"stderr","text":"/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","output_type":"stream"},{"name":"stdout","text":" all 1263 561 0.887 0.838 0.91 0.533\nSpeed: 0.1ms preprocess, 10.9ms inference, 0.0ms loss, 0.8ms postprocess per image\nResults saved to \u001b[1m/kaggle/working/road_damage_detection/run_damages_only\u001b[0m\nTraining finished!\n","output_type":"stream"}],"execution_count":8},{"cell_type":"code","source":"import os, shutil\n\nwdir = \"/kaggle/working/road_damage_detection/run_damages_only/weights\"\nprint(\"Weights dir exists:\", os.path.isdir(wdir))\nprint(\"Files:\", os.listdir(wdir))\n\nsrc = os.path.join(wdir, \"best.pt\")\ndst = \"/kaggle/working/best.pt\"\nif os.path.exists(src):\n shutil.copy2(src, dst)\n print(\"Copied to:\", dst)\n","metadata":{"trusted":true},"outputs":[],"execution_count":null}]} \ No newline at end of file diff --git a/submissions/T-075_Turinators/code/ml/readme.md b/submissions/T-075_Turinators/code/ml/readme.md new file mode 100644 index 00000000..98393a5f --- /dev/null +++ b/submissions/T-075_Turinators/code/ml/readme.md @@ -0,0 +1 @@ +readme for ml \ No newline at end of file diff --git a/submissions/T-075_Turinators/readme.md b/submissions/T-075_Turinators/readme.md new file mode 100644 index 00000000..e7703957 --- /dev/null +++ b/submissions/T-075_Turinators/readme.md @@ -0,0 +1,47 @@ + +# Vaahan-Saarthi 🚗🛣️(Open Innovation Challenge) + +## 🏷 Team Details +- **Team Name:** Turinators +- **Team ID:** T-075 + +### 👥 Members & Roles +- **Gurneet Singh** – Project Lead / ML Engineer +- **Geetansh Gatumn** – ML Engineer (YOLO Model Training) /Presenter +- **Manpreet Singh** – ML Engineer (YOLO Model Training) +- **Arshpreet Singh** – Frontend Developer (Visualization & UI) + +--- + +## 📌 Problem Statement +### India faces a severe challenge due to pothole-ridden roads. According to reports, **over 11,000 road accident deaths are linked to potholes**. Despite technological advances in automobiles, **there is no standard built-in solution in vehicles to detect and alert drivers about potholes**. + +#### Solution +Our project addresses this issue by leveraging **Computer Vision (YOLO-based object detection)** to automatically detect and **mark potholes from real-time road footage**. The system not only identifies potholes but also makes **driving suggestions such as slowing down or steering assistance**. + +Looking ahead, this solution can be integrated with **ADAS (Advanced Driver Assistance Systems)** in vehicles to give drivers a proactive advantage, especially in India where road infrastructure challenges are significant. This approach enables **safer driving, accident prevention, and proactive road maintenance**.. + +--- + +## 🛠 Tech Stack +- **Machine Learning:** YOLOv8 (custom trained model for pothole detection) +- **Backend:** Flask (serving detection results, for visualization and reporting) +- **Frontend:** React / Vite +- **Other Tools:** OpenCV, Numpy + +--- + +## 🚀 How to Run the Project + +### 1️⃣ Clone the Repository +#### backend +```bash +cd backend +pip install -r requirements.txt +flask run +``` +#### frontend +```bash +cd frontend +npm install +npm run dev