Instructions to use EscvNcl/MobileNet-V2-Retinopathy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EscvNcl/MobileNet-V2-Retinopathy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="EscvNcl/MobileNet-V2-Retinopathy") 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("EscvNcl/MobileNet-V2-Retinopathy") model = AutoModelForImageClassification.from_pretrained("EscvNcl/MobileNet-V2-Retinopathy") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:d3ba9852c125e374b2b26b51c1e10991a96715e9fd2c8a6ee7f1683788d63ff7
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size 17507576
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