📷 Upload an image → 🧠 YOLOv5 detects objects → 🌐 View results in your browser
- 🔍 Object detection using pre-trained YOLOv5
- 🖼️ Upload
.jpg/.jpeg/.pngimages via a Streamlit web app - 💡 Instantly displays bounding boxes, labels, and confidence scores
- 🌐 Hosted on
localhostfor easy local testing
| Tool | Purpose |
|---|---|
| Python | Programming Language |
| PyTorch | Loads the YOLOv5 model |
| OpenCV | Image reading and decoding |
| NumPy | Array and tensor operations |
| Streamlit | Web UI for file upload and display |
🔧 1. Clone the Repo git clone https://github.com/yourusername/rtod-yolov5.git cd rtod-yolov5
🌱 2. Create a Virtual Environment python -m venv yolov5env yolov5env\Scripts\activate # (Windows CMD)
📦 3. Install Dependencies pip install streamlit opencv-python torch torchvision torchaudio numpy seaborn
🚀 4. Run the App streamlit run app.py Then open http://localhost:8501 in your browser 🚀
🚌 Sometimes detects a bus as a car (common in COCO dataset confusion)
⚡ Not yet optimized for performance or large resolution images
🧪 YOLOv5s is the lightest model — might upgrade to yolov5m or yolov5x later
👷♀️ This project is a work in progress — I’ll keep improving the detection accuracy and optimizing the model behavior over time.
🔴 Real-time webcam detection
🎥 Live video stream support (via OpenCV or WebRTC)
© 2025 Rishika Kumari. All rights reserved.