Instructions to use toolevalxm/MedVisionNet-BestCheckpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/MedVisionNet-BestCheckpoint with Transformers:
# Load model directly from transformers import AutoImageProcessor, ResNet50ForImageClassification processor = AutoImageProcessor.from_pretrained("toolevalxm/MedVisionNet-BestCheckpoint") model = ResNet50ForImageClassification.from_pretrained("toolevalxm/MedVisionNet-BestCheckpoint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ebd7c4c814f94261c9f5578519f59a82a52f68404e71b0144f36db02b29569bf
- Size of remote file:
- 24 Bytes
- SHA256:
- ddae52a64b92601cfaece0a7af3ebb4630d32a84e64be70fdaae71df3fb1127b
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