---
title: ResNet50 ImageNet Classifier
emoji: 🖼️
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 5.9.1
app_file: app.py
pinned: false
---

# ResNet50 trained on ImageNet-1K

This is a ResNet50 model trained on ImageNet-1K dataset with 1000 classes. The model can classify a wide variety of images into 1000 different categories.

## Model Details

- Architecture: ResNet50
- Dataset: ImageNet-1K
- Classes: 1000
- Input Size: 224x224 pixels
- Model File: `resnet50_imagenet1k.pth`
- Training Repository: [Link](https://github.com/pradeep6kumar/ImageNet_v4)

## Quick Start

1. Clone the repository:
```bash
git clone https://huggingface.co/spaces/Shilpaj/ImageNet
cd ImageNet
```

2. Download the model:
```bash
# Option 1: Using wget
wget https://huggingface.co/spaces/Shilpaj/ImageNet/blob/main/resnet50_imagenet1k.pth

# Option 2: Manual download
Download from: https://huggingface.co/spaces/Shilpaj/ImageNet/tree/main/resnet50_imagenet1k.pth
```

3. Install requirements:
```bash
pip install -r requirements.txt
```

4. Run the demo:
```bash
python app.py
```

## Usage in Your Project

```python
from inference import ImageNetClassifier

# Initialize the classifier
classifier = ImageNetClassifier('resnet50_imagenet1k.pth')

# Classify an image
image_path = 'path/to/your/image.jpg'
prediction, confidence = classifier.predict(image_path)
print(f"Prediction: {prediction}")
print(f"Confidence: {confidence:.2f}%")
```

## Example Images

The `assets/examples` directory contains sample images for testing:
- Bird
- Car
- Cat
- Dog
- Frog
- Horse
- Plane
- Ship
- Truck

## Repository Structure

```
.
├── app.py              # Gradio web interface
├── inference.py        # Model inference code
├── requirements.txt    # Python dependencies
└── assets/
    └── examples/      # Example images for testing
```

## License
MIT

## Acknowledgments

- ImageNet Dataset
- PyTorch Team
- HuggingFace Datasets