---
title: Generative Augmented Classifiers
emoji: 💻
colorFrom: gray
colorTo: indigo
sdk: gradio
sdk_version: 4.36.1
app_file: app.py
pinned: false
---

# Generative Augmented Classifiers

Main GitHub Repo: [Generative Data Augmentation](https://github.com/zhulinchng/generative-data-augmentation) | Image Classification Demo: [Generative Augmented Classifiers](https://huggingface.co/spaces/czl/generative-augmented-classifiers).

This demo showcases the performance of image classifiers trained on various datasets as part of the project `Improving Fine-Grained Image Classification Using Diffusion-Based Generated Synthetic Images' dissertation.

## Demo Usage Instructions

1. Select the dataset, the model architecture, training methods, type of training dataset to evaluate the classifier on.
2. Upload an image, or click  `Sample Random Image` to select a random image from the validation dataset.
3. Click `Classify` to classify the image using the selected classifier.
4. To download the classifier, click `Download Model: <model_name>`.

The top 5 predicted labels and their corresponding probabilities are displayed.

## Configuration

```bash
git clone https://huggingface.co/spaces/czl/generative-augmented-classifiers
cd generative-data-augmentation-demo
# Setup the data directory structure as shown above
conda create --name $env_name python=3.11.* # Replace $env_name with your environment name
conda activate $env_name
# Visit PyTorch website https://pytorch.org/get-started/previous-versions/#v212 for PyTorch installation instructions.
pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url # Obtain the correct URL from the PyTorch website
pip install -r requirements.txt
python app.py
```