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---
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title: Generative Augmented Classifiers
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emoji: 💻
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colorFrom: gray
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colorTo: indigo
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sdk: gradio
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sdk_version: 4.36.1
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app_file: app.py
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pinned: false
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---
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# Generative Augmented Classifiers
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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).
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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.
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## Demo Usage Instructions
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1. Select the dataset, the model architecture, training methods, type of training dataset to evaluate the classifier on.
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2. Upload an image, or click `Sample Random Image` to select a random image from the validation dataset.
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3. Click `Classify` to classify the image using the selected classifier.
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4. To download the classifier, click `Download Model: <model_name>`.
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The top 5 predicted labels and their corresponding probabilities are displayed.
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## Configuration
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```bash
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git clone https://huggingface.co/spaces/czl/generative-augmented-classifiers
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cd generative-data-augmentation-demo
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# Setup the data directory structure as shown above
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conda create --name $env_name python=3.11.* # Replace $env_name with your environment name
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conda activate $env_name
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# Visit PyTorch website https://pytorch.org/get-started/previous-versions/#v212 for PyTorch installation instructions.
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pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url # Obtain the correct URL from the PyTorch website
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pip install -r requirements.txt
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python app.py
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```
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