---
title: CIFAR-100 ResNet-18 Classifier
emoji: 🖼️
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: ResNet-18 for CIFAR-100 classification (100 categories)
tags:
  - computer-vision
  - image-classification
  - pytorch
  - resnet
  - cifar-100
---

# 🖼️ CIFAR-100 ResNet-18 Image Classifier

This Hugging Face Space demonstrates a ResNet-18 model optimized for CIFAR-100 image classification. The model can classify images into 100 different categories including animals, vehicles, plants, and household objects.

## 🚀 Features

- **Custom ResNet-18 Architecture**: Adapted specifically for CIFAR-100's 32×32 input resolution
- **100 Class Classification**: Covers a wide range of categories from the CIFAR-100 dataset
- **Interactive Interface**: Easy-to-use Gradio interface for real-time predictions
- **Top-5 Predictions**: Shows confidence scores for the most likely classes
- **Technical Details**: Comprehensive model information and architecture details

## 🏗️ Model Architecture

The model uses a modified ResNet-18 architecture with the following key features:

- **Input**: 32×32 RGB images (automatically resized)
- **Residual Blocks**: Skip connections for improved gradient flow
- **Batch Normalization**: Stable training and inference
- **No Max Pooling**: Optimized stem for small input resolution
- **Adaptive Pooling**: Global average pooling before classification
- **100 Output Classes**: Full CIFAR-100 category coverage

## 📊 Training Features

The training pipeline includes advanced techniques:

- **OneCycle Learning Rate**: Cyclical learning rate scheduling
- **Mixed Precision**: Automatic mixed precision for efficiency
- **Data Augmentation**: Random crops, flips, and color jittering
- **Label Smoothing**: Improved generalization
- **Gradient Clipping**: Stable training dynamics
- **Grad-CAM Visualization**: Built-in attention visualization

## 🎯 CIFAR-100 Categories

The model classifies images into 100 categories including:

**Animals**: apple, aquarium_fish, baby, bear, beaver, bee, beetle, butterfly, camel, cattle, chimpanzee, cockroach, crab, crocodile, dinosaur, dolphin, elephant, flatfish, fox, hamster, kangaroo, leopard, lion, lizard, lobster, mouse, otter, porcupine, possum, rabbit, raccoon, ray, seal, shark, shrew, skunk, snail, snake, spider, squirrel, tiger, trout, turtle, whale, wolf, worm

**Plants**: maple_tree, oak_tree, palm_tree, pine_tree, willow_tree, orchid, poppy, rose, sunflower, tulip, orange, pear, sweet_pepper, mushroom

**Vehicles**: bicycle, bus, motorcycle, pickup_truck, train, streetcar, tank, tractor, rocket

**Household**: bed, bottle, bowl, can, chair, clock, couch, cup, house, keyboard, lamp, plate, table, telephone, television, wardrobe

**Others**: bridge, castle, cloud, forest, mountain, plain, road, sea, skyscraper

## 🔧 Usage

1. Upload an image using the interface
2. The model will automatically resize it to 32×32 pixels
3. Get top-5 predictions with confidence scores
4. View detailed model information and technical specifications

## 📁 Repository Structure

```
era-resnet/
├── app.py              # Gradio interface
├── model.py            # ResNet-18 architecture
├── train.py            # Training script with advanced features
├── requirements.txt    # Python dependencies
├── train.log          # Training history and metrics
└── README.md          # This file
```

## 🚀 Local Development

To run this app locally:

```bash
pip install -r requirements.txt
python app.py
```

To train your own model:

```bash
python train.py --epochs 100 --batch-size 128 --max-lr 0.1
```

## 📈 Training Performance

The model achieves competitive performance on CIFAR-100:
- Training includes 100 epochs with OneCycle scheduling
- Mixed precision training for efficiency
- Grad-CAM visualizations saved during training
- Comprehensive logging and checkpointing

## 🤝 Contributing

Feel free to experiment with:
- Different architectures (modify `model.py`)
- Hyperparameter tuning (see `train.py` arguments)
- Additional data augmentation techniques
- Transfer learning from other datasets

## 📄 License

This project is licensed under the Apache 2.0 License.