Instructions to use toolevalxm/MedVision-X-TestRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/MedVision-X-TestRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="toolevalxm/MedVision-X-TestRepo") 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-X-TestRepo") model = AutoModelForImageClassification.from_pretrained("toolevalxm/MedVision-X-TestRepo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from toolevalxm/MedVision-X-TestRepo: direct link, hf CLI and curl.
- Browser
- Download file 153 Bytes
-
https://huggingface.co/toolevalxm/MedVision-X-TestRepo/resolve/main/config.json
- Command line
-
hf download hf://toolevalxm/MedVision-X-TestRepo/config.json
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curl -L -o config.json https://huggingface.co/toolevalxm/MedVision-X-TestRepo/resolve/main/config.json
153 Bytes
| { | |
| "model_type": "resnet", | |
| "architectures": [ | |
| "ResNetForImageClassification" | |
| ], | |
| "num_labels": 14, | |
| "image_size": 224, | |
| "hidden_size": 2048 | |
| } |