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
File size: 253 Bytes
dbd8f94 | 1 2 3 4 5 6 7 8 9 10 11 | {
"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
} |