README.md

metadata

title: GEMS Generalized Raman Classifier
emoji: 📊
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
license: mit

📊 GEMS: Generalized Raman Classifier Webserver

Welcome to the official interactive web application for GEMS (Foundation Models for Universal, Data-efficient Classification of Raman Spectra).

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.

🔗 Quick Links

• Source Code: Explore the core implementation, model architecture, and local deployment instructions in our GitHub Repository.
• Datasets: Access the complete, pre-processed multi-stage datasets (including pre-training, contrastive learning, and diverse downstream tasks) at our Hugging Face Dataset Repo.

🚀 How to Test it Out?

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.

Step-by-step Guide:

1. Visit our 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.