README.md

metadata

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

Quick Start

1. Clone the repository:

git clone https://huggingface.co/spaces/Shilpaj/ImageNet
cd ImageNet

2. Download the model:

# 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:

pip install -r requirements.txt

4. Run the demo:

python app.py

Usage in Your Project

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