| --- |
| title: GEMS Generalized Raman Classifier |
| emoji: 📊 |
| colorFrom: blue |
| colorTo: purple |
| sdk: docker |
| pinned: false |
| license: mit |
| --- |
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| # 📊 GEMS: Generalized Raman Classifier Webserver |
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| Welcome to the official interactive web application for **GEMS (Foundation Models for Universal, Data-efficient Classification of Raman Spectra)**. |
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| This Space provides a highly convenient, no-code graphical interface. It empowers researchers and practitioners to seamlessly perform fine-tuning, automated evaluation, and rapid predictions using the powerful GEMS foundation model. |
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| ## 🔗 Quick Links |
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| * **Source Code:** Explore the core implementation, model architecture, and local deployment instructions in our [GitHub Repository](https://github.com/JunhanCai/GEMS). |
| * **Datasets:** Access the complete, pre-processed multi-stage datasets (including pre-training, contrastive learning, and diverse downstream tasks) at our [Hugging Face Dataset Repo](https://huggingface.co/datasets/JunhanCai/GEMS-Raman-Dataset). |
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| ## 🚀 How to Test it Out? |
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| You can test this web server immediately without preparing your own data! We have provided several pre-processed downstream datasets that are 100% compatible with this interface. |
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| **Step-by-step Guide:** |
| 1. Visit our [GEMS-Raman-Dataset](https://huggingface.co/datasets/JunhanCai/GEMS-Raman-Dataset) page. |
| 2. Download any downstream task subset that interests you (e.g., the `.npy` files from the `microplastic`, `skincancer`, or `Bacteria_ID` folders). |
| 3. Return to this Space and upload the downloaded `spectral.npy`, `labels.npy`, and `wavenumbers.npy` files into the **Fine-tune** panel. |
| 4. *(Optional)* Leave the `Pretrained Model` field empty! The server will automatically load our built-in foundation weights. |
| 5. Click **Start Fine-Tuning Job** to witness the automated AutoML pipeline and get your classification reports. |
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| **Note:** Due to the hardware constraints of the free Hugging Face CPU tier, training may take some time. For production-level speed, we highly recommend pulling the Docker image or source code from our GitHub and deploying it locally on a GPU-enabled machine. |
