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 config.json from toolevalxm/MedVision-RadNet-BestModel: direct link, hf CLI and curl.
- Browser
- Download file 253 Bytes
-
https://huggingface.co/toolevalxm/MedVision-RadNet-BestModel/resolve/main/config.json
- Command line
-
hf download hf://toolevalxm/MedVision-RadNet-BestModel/config.json
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curl -L -o config.json https://huggingface.co/toolevalxm/MedVision-RadNet-BestModel/resolve/main/config.json
253 Bytes
| { | |
| "model_type": "vit", | |
| "architectures": ["ViTForImageClassification"], | |
| "hidden_size": 768, | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "image_size": 512, | |
| "patch_size": 16, | |
| "num_channels": 1, | |
| "num_labels": 15 | |
| } |