- I gratuated from Télécom Paris and KTH.
- I studied machine learning for computer vision and natural language processing.
- I do computer graphics stuff in my free time.
Currently working on efficient architectures for sentence embedding.
A Python tool to evaluate the performance of VLM on the medical domain.
License: MIT License
Currently working on efficient architectures for sentence embedding.
Hello, thanks for your excellent work! I'd love to try your work.
However, I can't access kaggle or Huggingface from my server because of network problems. I can only download the dataset locally first.
How do I load the dataset locally instead of downloading it from the network? Do I need to process this dataset? For example, extract the answers in a certain format. Question equivalence?
If possible, can you give a process and method that can be referenced?
Thank you very much for your help!
Sorry to bother you again! But I have some questions about the format of the input and output data as follows:
Is this the image path? Read it as PIL.image(path)?
and
What is the json format of answers? Can you give me a specific example?
I am very sorry to bother you all the time, and I would really appreciate your reply.
Add ability to have stratified results when possible:
Add ability to also take care of: fairness metrics such as balance, equality, and disparity. (Refer: https://arxiv.org/pdf/2310.09507.pdf)
Do you have any plans to evaluate more multimodal medical models? E.g. BiomedCLIP, PubMedCLIP, etc.?
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