https://github.com/parthapray/yolo11_stanford_dog_detection_huggingface
This repo contains codes for Yolo11 fine tuning for Stanford dataset dog detection hosted on Huggin Face Space
https://github.com/parthapray/yolo11_stanford_dog_detection_huggingface
detection dog finetune gradio huggingface space stanford ultralytics yolo11
Last synced: 8 months ago
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This repo contains codes for Yolo11 fine tuning for Stanford dataset dog detection hosted on Huggin Face Space
- Host: GitHub
- URL: https://github.com/parthapray/yolo11_stanford_dog_detection_huggingface
- Owner: ParthaPRay
- License: mit
- Created: 2024-12-23T15:22:36.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-12-23T15:37:14.000Z (10 months ago)
- Last Synced: 2025-01-09T23:54:47.432Z (9 months ago)
- Topics: detection, dog, finetune, gradio, huggingface, space, stanford, ultralytics, yolo11
- Language: Jupyter Notebook
- Homepage:
- Size: 8.95 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# YOLO Dog Breed Detection Web App
## Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Demo](#demo)
- [Installation](#installation)
- [Prerequisites](#prerequisites)
- [Clone the Repository](#clone-the-repository)
- [Set Up the Environment](#set-up-the-environment)
- [Install Dependencies](#install-dependencies)
- [Usage](#usage)
- [Running the Application](#running-the-application)
- [Interacting with the Web App](#interacting-with-the-web-app)
- [Metrics Handling](#metrics-handling)
- [Project Structure](#project-structure)
- [Troubleshooting](#troubleshooting)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
## Overview
The **YOLO Dog Breed Detection Web App** is a powerful and user-friendly tool designed to detect and classify dog breeds within uploaded images using the YOLO (You Only Look Once) object detection framework. Leveraging a pre-trained YOLO model with 120 dog breed classes, this web application provides real-time detection results, including bounding boxes, confidence scores, and comprehensive validation metrics.
## Features
- **Real-Time Detection**: Upload an image and receive instant detection results with bounding boxes and confidence scores.
- **Comprehensive Metrics**: View both overall and per-class validation metrics to assess model performance.
- **User-Friendly Interface**: Built with Gradio for an intuitive and interactive user experience.
- **Scalable Architecture**: Designed to handle a large number of classes efficiently.
- **High Accuracy**: Utilizes a robust YOLO model trained on a diverse dog breed dataset.
## Dataset
https://universe.roboflow.com/iliescu-mihail-doirn/stanford-dogs-dataset-dog-breed/dataset/1
## Demo
*Screenshot demonstrating the YOLO Dog Breed Detection Web App in action.*
## Installation
### Prerequisites
- **Python 3.10.12** or higher
- **CUDA 12.1** (for GPU acceleration) *(Optional but recommended for faster inference)*
- **Git** (to clone the repository)
### Clone the Repository
```bash
git clone https://github.com/yourusername/your-repo-name.git
cd your-repo-name
```
### Set Up the Environment
It's recommended to use a virtual environment to manage dependencies.
#### Using `venv`:
```bash
python -m venv venv
```
#### Activate the Virtual Environment:
- **Windows:**
```bash
venv\Scripts\activate
```
- **macOS and Linux:**
```bash
source venv/bin/activate
```
### Install Dependencies
Ensure you have `pip` updated to the latest version:
```bash
pip install --upgrade pip
```
Install the required Python packages using the provided `requirements.txt`:
```bash
pip install -r requirements.txt
```
**Note:** The `torch` package specified in `requirements.txt` is built with CUDA 12.1 support. If your system has a different CUDA version or if you're using a CPU-only setup, adjust the PyTorch installation accordingly. Visit the [PyTorch Official Installation Page](https://pytorch.org/get-started/locally/) for the appropriate command.
For example, for CPU-only support:
```bash
pip install torch==2.5.1 torchvision==0.15.2 torchaudio==2.5.1
```
## Usage
### Running the Application
Ensure that the `best.pt` YOLO model file and the `data.yaml` configuration file are present in the project directory.
Execute the Python script to launch the web application:
```bash
python app.py
```
*Replace `app.py` with the actual filename if different.*
### Interacting with the Web App
1. **Access the Web Interface:**
After running the script, Gradio will provide a local URL (e.g., `http://127.0.0.1:7860/`). Open this URL in your web browser.
2. **Upload an Image:**
- Click on the "Upload Image" button.
- Select an image of a dog from your local machine.
3. **Run Inference:**
- Click the "Run Inference" button.
- The application will process the image, detect dog breeds, and display the results.
4. **View Results:**
- **Annotated Image:** Displays the uploaded image with bounding boxes and labels indicating detected breeds and confidence scores.
- **Detection Results:** A table listing detected classes with their confidence scores and bounding box coordinates.
- **Validation Metrics:** A table showing overall and per-class metrics to evaluate model performance.
## Metrics Handling
The application displays both **overall** and **per-class** metrics to provide a comprehensive view of the model's performance.
- **Overall Metrics:**
- **Precision:** The ratio of true positive detections to the total predicted positives.
- **Recall:** The ratio of true positive detections to the actual positives.
- **mAP50:** Mean Average Precision at 50% IoU threshold.
- **mAP50-95:** Mean Average Precision across IoU thresholds from 50% to 95%.
- **Per-Class Metrics:**
- Detailed metrics for each of the 120 dog breed classes, including Precision, Recall, mAP50, and mAP50-95.
**Note:** Metrics are precomputed and hardcoded into the application for demonstration purposes. For real-world applications, consider implementing dynamic metric computation.
## Project Structure
```
your-repo-name/
│
├── app.py # Main application script
├── best.pt # Pre-trained YOLO model
├── data.yaml # YOLO data configuration file
├── requirements.txt # Python dependencies
├── README.md # Project documentation
```
## Troubleshooting
- **Model File Not Found:**
Ensure that `best.pt` is located in the project directory or update the `model_path` variable in `app.py` to the correct location.
- **CUDA Errors:**
If you encounter CUDA-related errors, verify that your system has the appropriate CUDA version installed. If not using a GPU, adjust the PyTorch installation to CPU-only as mentioned in the [Installation](#installation) section.
- **Dependencies Issues:**
Ensure all dependencies are installed correctly. You can reinstall them using:
```bash
pip install --force-reinstall -r requirements.txt
```
- **Gradio Not Launching:**
Check if the required ports are free and not blocked by firewalls. You can specify a different port by modifying the `demo.launch()` line:
```python
demo.launch(server_port=7861)
```
## License
This project is licensed under the [MIT License](LICENSE).
## Contact
For any questions or suggestions, please contact:
- **Name:** Partha Pratim Ray
- **Email:** parthapratimray1986@gmail.com
---
## Hugging Face Space
https://huggingface.co/spaces/csepartha/yolo11_stanford_dog_detection
*Happy Detecting! 🐶🔍*