Transformers
PyTorch
Safetensors
English
fundus
diabetic retinopathy
classification
Eval Results (legacy)
Instructions to use ClementP/FundusDRGrading-efficientnet_b2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ClementP/FundusDRGrading-efficientnet_b2 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ClementP/FundusDRGrading-efficientnet_b2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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tags:
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This model has been pushed to the Hub using ****:
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- Repo: [More Information Needed]
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- Docs: [More Information Needed]
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---
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language: en
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license: mit
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tags:
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- fundus
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- diabetic retinopathy
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- classification
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datasets:
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- APTOS
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- EYEPACS
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- IDRID
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- DDR
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library: timm
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model-index:
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- name: efficientnet_b2
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results:
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- task:
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type: image-classification
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dataset:
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name: EYEPACS
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type: EYEPACS
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metrics:
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- type: kappa
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value: 0.7577868700027466
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name: Quadratic Kappa
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- task:
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type: image-classification
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dataset:
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name: IDRID
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type: IDRID
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metrics:
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- type: kappa
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value: 0.7042314410209656
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name: Quadratic Kappa
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- task:
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type: image-classification
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dataset:
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name: DDR
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type: DDR
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metrics:
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- type: kappa
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value: 0.7354801893234253
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name: Quadratic Kappa
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---
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# Fundus DR Grading
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[](https://rye-up.com)
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[](https://pytorch.org/docs/stable/index.html)
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[](https://lightning.ai/docs/pytorch/stable/)
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## Description
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This project aims to evaluate the performance of different models for the classification of diabetic retinopathy (DR) in fundus images. The reported perfomance metrics are not always consistent in the literature. Our goal is to provide a fair comparison between different models using the same datasets and evaluation protocol.
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