I made this for educational purposes, exploring ResNet architectures, custom training loops, and hands-on deep learning implementations of Image Classification.
⭐ Star this repo if it helps you! ⭐
🔥 Share it with the community! 🔥
🤖 Cutting-edge AI image classification powered by ResNet-50!
| Accuracy | Model | Framework | Author |
|---|---|---|---|
| 92.5% | ResNet-50 | Gradio | Mushrum-mmb |
- State-of-the-art accuracy at 92.5%.
- Real-time predictions with confidence scores
- Web-based interface for easy access
- Comment tutoring support in code
- Cross-platform compatibility
*Note: This exceptional accuracy is calculated from validation datasets. Real-world performance may vary. *
Confusion matrix
Tensorboard quick view and comparison
Examples of successful animal image classification
| Feature | Description | Benefit |
|---|---|---|
| Image Classification | Upload images for instant animal category prediction | Quick and accurate results |
| Pre-trained Model | ResNet-50 architecture fine-tuned on animal datasets | Superior accuracy and reliability |
| Real-time Inference | Instant predictions with confidence percentages | Immediate feedback for users |
| GPU Acceleration | Automatic GPU detection and utilization | Lightning-fast processing |
| Easy Deployment | One-command launch with public sharing option | Hassle-free setup and sharing |
| Google Colab Ready | Optimized for cloud-based training and testing | Perfect for low-spec devices |
Step 1: Clone the repository
git clone https://github.com/Mushrum-mmb/ResNet-Implementation-Drafts.gitStep 2: Navigate to project directory
cd ResNet-Implementation-DraftsStep 3: Install the requirements
pip install -r requirements.txtStep 4: Launch the application
python app.py🎉 Your AI is Ready! Open the provided link in your browser and start classifying images!
Can't run AI on your device? No problem! Use our optimized Google Colab notebook for seamless cloud-based AI training and inference.
📖 Colab Guide (Click to expand)
Just execute the first and second cell
Launch and enjoy! 🎉
Our AI system consists of five core components working in harmony:
graph TD
A[Image Input] --> C[datasets.py]
C --> D[train.py]
D --> E[test.py]
E --> F[app.py]
F --> G[Gradio Interface]
| Component | Purpose | Key Features |
|---|---|---|
| datasets.py | Data Management | • Custom PyTorch dataset class • Image normalization and transforms • Train/test data splitting |
| train.py | Model Training | • ResNet-50 architecture implementation • TensorBoard logging integration • Automatic checkpoint saving |
| test.py | Model Testing | • Single image inference • Confidence score calculation • Visual result display |
| app.py | Web Interface | • Gradio-powered UI • Real-time predictions • Public sharing capabilities |
We love contributions from the community! Here's how you can help:
- Report bugs or suggest features
- Submit pull requests with improvements
- Improve documentation and tutorials
- Share your results and use cases
- Star the repo to show support!
This project is licensed under the MIT License - see the LICENSE file for details.