README.md

metadata

title: ResNetonImageNet
emoji: 🏢
colorFrom: blue
colorTo: blue
sdk: gradio
sdk_version: 5.9.1
app_file: app.py
pinned: false

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

ResNet50 Image Classifier

This is a Gradio web application that uses a trained ResNet50 model to classify images. The application provides real-time predictions with top-3 confidence scores for uploaded images.

Live Demo

Visit the application at [Hugging Face Spaces URL]

Features

• Real-time image classification
• Top-3 predictions with confidence scores
• Support for various image formats
• User-friendly interface
• Detailed prediction logging
• Example images for testing

Using the Application

Quick Start

1. Visit the Hugging Face Space
2. Upload an image using one of these methods:
   • Click the "Upload Image" button
   • Drag and drop an image into the input area
   • Use the provided example images

Input Requirements

• Supported formats: JPG, PNG, BMP
• Both color and grayscale images accepted
• Images are automatically:
  • Resized to 256 pixels
  • Center cropped to 224x224
  • Normalized using ImageNet statistics

Output Format

The model returns:

1. Predicted Class: The most likely class
2. Top 3 Predictions: Three most likely classes with confidence scores

Example output:

Predicted Class: dog
Top 3 Predictions:
dog: 95.32%
cat: 3.45%
fox: 1.23%

Technical Details

Model Architecture

• Base model: ResNet50
• Input size: 224x224 pixels
• Output: Class probabilities through softmax
• Model format: PyTorch (.pth)

Image Processing Pipeline

transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    )
])

File Structure

.
├── app.py              # Main application file
├── requirements.txt    # Dependencies
├── README.md          # Documentation
├── src/
│   └── model_10.pth   # Trained model weights
│   └── classes.txt    # Class labels
├── models/
│   └── model_n.pth    # other models
└── examples/          # Example images
    ├── example1.jpg
    └── example2.jpg

Deployment Guide

Prerequisites

1. Hugging Face account
2. Trained ResNet50 model (.pth format)
3. Class labels file (classes.txt)
4. Example images (optional)

Deployment Steps

1. Create a new Space:

   • Go to huggingface.co/spaces
   • Click "Create new Space"
   • Select "Gradio" as the SDK
   • Use the provided space configuration from this README

2. Upload required files:

   • All files from the File Structure section
   • Ensure correct file paths in app.py

3. The Space will automatically build and deploy

Space Configuration

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

Troubleshooting

Common Issues

1. Model Loading Errors

   • Verify model path in app.py
   • Check model format and class count

2. Image Upload Issues

   • Verify supported formats
   • Check image file size

3. Prediction Errors

   • First prediction may be slower (model loading)
   • Check input image quality

Performance Notes

• CPU inference by default
• GPU supported if available
• Batch processing not supported
• Real-time predictions

Development

Requirements

torch>=2.0.0
torchvision>=0.15.0
gradio>=4.19.2
Pillow>=9.0.0
numpy>=1.21.0

Local Development

1. Clone the repository
2. Install dependencies:

   pip install -r requirements.txt
   

3. Run locally:

   python app.py