Instructions to use toolevalxm/MedVision-RadNet-BestModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/MedVision-RadNet-BestModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="toolevalxm/MedVision-RadNet-BestModel") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("toolevalxm/MedVision-RadNet-BestModel") model = AutoModelForImageClassification.from_pretrained("toolevalxm/MedVision-RadNet-BestModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from toolevalxm/MedVision-RadNet-BestModel: direct link, hf CLI and curl.
- Browser
- Download file 81 Bytes
-
https://huggingface.co/toolevalxm/MedVision-RadNet-BestModel/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://toolevalxm/MedVision-RadNet-BestModel/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/toolevalxm/MedVision-RadNet-BestModel/resolve/main/pytorch_model.bin
81 Bytes
- Xet hash:
- fa0e31c698c2f8d3d1a600562d8c22505fed818ebf737c7c19cf34cfd9e0bddd
- Size of remote file:
- 81 Bytes
- SHA256:
- c57b334f5594dcb157ee268ffa15ca900bb3a2365201c8c54b0de5b634211aa8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.