Instructions to use bswift/RETfound_eyepacs_DR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bswift/RETfound_eyepacs_DR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bswift/RETfound_eyepacs_DR") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("bswift/RETfound_eyepacs_DR") model = AutoModelForImageClassification.from_pretrained("bswift/RETfound_eyepacs_DR") - Notebooks
- Google Colab
- Kaggle
eyepacs-2015-2019-12-12-2023-093828/-12-12-2023-093828
Description
Fine-tuned vit_large_patch16 model for ./models/eyepacs-2015-2019-12-12-2023-093828/
Use Cases
- RETfound validation
- Diabetic screening
Limitations
- for research/education use only
- not for clinical use
Ethics
- Ethics 1
- Ethics 2
Training Data
6999 images from 5 classes
Training Procedure
Fine-tuned for 50 epochs with batch size 10 and base learning rate 0.005
Intended Use
Intended for use with eyepacs-2015-2019 dataset
Authors
- Author 1
- Author 2
References
- Reference 1
- Reference 2
- Downloads last month
- 35