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README.md
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license: mit
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language:
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- [Training the Model](#training-the-model)
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- [Troubleshooting](#troubleshooting)
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- [Contributing](#contributing)
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## Project Overview
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This application predicts glaucoma severity (Normal, Mild, Moderate, Severe) from retinal images by estimating the Cup-to-Disc Ratio (CDR) and providing confidence scores. The backend, served via Flask on port 5000, uses a pre-trained ResNet101 model. The frontend, served on port 8000, allows users to upload images and view results. The model is available at [Hugging Face](https://huggingface.co/5t4l1n/ai-eye-disease-detection/blob/main/model/best_glaucoma_model.pth), and the dataset includes images in `dataset/G1020/Images_Square/` (e.g., `237.jpg`).
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2. Branch: `git checkout -b feature/your-feature`
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3. Commit: `git commit -m "Add your feature"`
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4. Push: `git push origin feature/your-feature`
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5. Open a pull request.
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## License
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MIT License. See [LICENSE](LICENSE).
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---
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language:
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- en
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- ta
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- [Training the Model](#training-the-model)
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- [Troubleshooting](#troubleshooting)
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- [Contributing](#contributing)
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## Project Overview
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This application predicts glaucoma severity (Normal, Mild, Moderate, Severe) from retinal images by estimating the Cup-to-Disc Ratio (CDR) and providing confidence scores. The backend, served via Flask on port 5000, uses a pre-trained ResNet101 model. The frontend, served on port 8000, allows users to upload images and view results. The model is available at [Hugging Face](https://huggingface.co/5t4l1n/ai-eye-disease-detection/blob/main/model/best_glaucoma_model.pth), and the dataset includes images in `dataset/G1020/Images_Square/` (e.g., `237.jpg`).
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2. Branch: `git checkout -b feature/your-feature`
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3. Commit: `git commit -m "Add your feature"`
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4. Push: `git push origin feature/your-feature`
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5. Open a pull request.
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