readme: # MedCLIP: Fine-tuning a CLIP model on the ROCO medical dataset
  
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    <!-- <p>MedCLIP</p> -->
    <img src="./assets/logo.png" alt="huggingface-medclip" width="250" height="250">
  
  ## Summary
  This repository contains the code for fine-tuning a CLIP model on the [ROCO dataset](https://github.com/razorx89/roco-dataset), a dataset made of radiology images and a caption.
  This work is done as a part of the [**Flax/Jax community week**](https://github.com/huggingface/transformers/blob/master/examples/research_projects/jax-projects/README.md#quickstart-flax-and-jax-in-transformers) organized by Hugging Face and Google.
  
  ### Demo
  You can try a Streamlit demo app that uses this model on [🤗 Spaces](https://huggingface.co/spaces/kaushalya/medclip-roco). You may have to signup for 🤗 Spaces private beta to access this app (screenshot shown below).
  ![Streamlit app](./assets/streamlit_app.png)
  
  🤗 Hub Model card: https://huggingface.co/flax-community/medclip-roco
  ## Dataset 🧩
  
  Each image is accompanied by a textual caption. The caption length varies from a few characters (a single word) to 2,000 characters (multiple sentences). During preprocessing we remove all images that has a caption shorter than 10 characters.
  Training set: 57,780 images with their caption.
  Validation set: 7,200
  Test set: 7,650
  
  [ ] Give an example
  
  ## Installation 💽
  This repo depends on the master branch of [Hugging Face - Transformers library](https://github.com/huggingface/transformers). First you need to clone the transformers repository and then install it locally (preferably inside a virtual environment) with `pip install -e ".[flax]"`.
  
  ## The Model ⚙️
  You can load the pretrained model from the Hugging Face Hub with
  ```
  from medclip.modeling_hybrid_clip import FlaxHybridCLIP
  
  model = FlaxHybridCLIP.from_pretrained("flax-community/medclip-roco")
  ```
  ## Training
  The model is trained using Flax/JAX on a cloud TPU-v3-8. 
  You can fine-tune a CLIP model implemented in Flax by simply running `sh run_medclip`.
  This is the validation loss curve we observed when we trained the model using the `run_medclip.sh` script.
  ![Validation loss](./assets/val_loss.png)
  
  ## Limitations 🚨
  The current model is capable of identifying if a given radiology image is a PET scan or an ultrasound scan. However it fails at identifying a brain scan from a lung scan. ❗️This model **should not** be used in a medical setting without further evaluations❗️.
  
  ## Acknowledgements
  Huge thanks to the Hugging Face 🤗 team and Google JAX/Flax team for organizing the community week and letting us use cloud compute for 2 weeks. We specially thank [@patil-suraj](https://github.com/patil-suraj) & [@patrickvonplaten](https://github.com/patrickvonplaten) for the continued support on Slack and the detailed feedback.
  
  
  ## TODO
  [ ] Evaluation on down-stream tasks
  
  [ ] Zero-shot learning performance
  
  [ ] Merge the demo app
  
  