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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- precision |
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library_name: transformers |
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pipeline_tag: image-classification |
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--- |
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# Acne Severity Detection Model |
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## Overview |
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This model card provides documentation for the Acne Severity Detection model checkpoint used in the Hugging Face pipeline. The model is designed to assess acne severity levels, ranging from clear skin to very severe acne. |
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## Model Details |
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The checkpoint includes the following files: |
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- **`config.json`**: Model configuration settings. |
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- **`model.safetensors`**: Serialized model parameters and architecture. |
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- **`optimizer.pt`**: Optimizer state capturing the current model optimization. |
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- **`preprocessor_config.json`**: Configuration file for the preprocessor. |
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- **`rng_state.pth`**: Random number generator state for reproducibility. |
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- **`scheduler.pt`**: Scheduler state for controlling learning rate schedules. |
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- **`trainer_state.json`**: Trainer state with information about the training process. |
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- **`training_args.bin`**: Binary file storing training arguments. |
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## Usage |
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To utilize the model checkpoint, follow these steps: |
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1. Clone or download this repository. |
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2. Ensure the required dependencies are installed (`pip install -r requirements.txt`). |
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3. Access the Streamlit app by running `streamlit run app.py`. |
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4. Interact with the model through the user-friendly interface. |
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## Important Notes |
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- The model card provides insight into the model's purpose, capabilities, and usage instructions. |
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- Ensure all necessary files are present in the `checkpoint` directory for proper functionality. |
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## License |
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This Acne Severity Detection model checkpoint is licensed under the [MIT License](LICENSE). Please review and adhere to the license when using or modifying the code. |
