README.md

---
title: ImageNet1k
emoji: 🚀 🌟 
colorFrom: red
colorTo: gray
sdk: gradio
sdk_version: 5.9.1
app_file: app.py
pinned: false
---

# ImageNet1k Classification Demo

This is a Gradio web application that demonstrates image classification using a ResNet50 model trained on the ImageNet1k dataset. The model can classify images into 1000 different categories.

## Features

- Upload and classify any image
- Get top 5 predictions with confidence scores
- Real-time inference
- User-friendly interface
- Example images included

## Technical Details

### Model Architecture
- Base Model: ResNet50
- Training Dataset: ImageNet1k (1000 classes)
- Input Size: 224x224 pixels
- Preprocessing: Standard ImageNet normalization (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

### Dependencies
- gradio: Web interface framework
- torch: PyTorch deep learning framework
- torchvision: Computer vision utilities
- Pillow: Image processing

## Usage

1. Upload an image using the interface
2. The model will process the image and return:
   - Top 5 predicted classes
   - Confidence scores for each prediction

## Tips for Best Results

- Use clear, well-lit images
- Ensure the main subject is centered and clearly visible
- The model works best with common objects, animals, and scenes
- Both color and black & white images are supported
- Images will be automatically resized to 224x224

## Local Setup

1. Clone the repository
2. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```
3. Place your trained model weights as `model_best.pth.tar` in the root directory
4. Run the application:
   ```bash
   python app.py
   ```

## Model Weights

The model weights (`model_best.pth.tar`) should be placed in the same directory as `app.py`. The weights file contains a ResNet50 model trained on ImageNet1k.

## Links

- [GitHub Repository](https://github.com/dhairyag/ImageNet1k_ResNet50)
- [Hugging Face Space](https://huggingface.co/spaces/dhairyashil/ImageNet1k)