Instructions to use jaypratap/vit-pretraining-2024_03_25-effusion-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaypratap/vit-pretraining-2024_03_25-effusion-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jaypratap/vit-pretraining-2024_03_25-effusion-classifier") 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("jaypratap/vit-pretraining-2024_03_25-effusion-classifier") model = AutoModelForImageClassification.from_pretrained("jaypratap/vit-pretraining-2024_03_25-effusion-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from jaypratap/vit-pretraining-2024_03_25-effusion-classifier: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/jaypratap/vit-pretraining-2024_03_25-effusion-classifier/resolve/main/model.safetensors
- Command line
-
hf download hf://jaypratap/vit-pretraining-2024_03_25-effusion-classifier/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/jaypratap/vit-pretraining-2024_03_25-effusion-classifier/resolve/main/model.safetensors
343 MB
- Xet hash:
- c3f0990a2561a335fe174e95f22b523cc8c4a8334bce78b630f396876be65128
- Size of remote file:
- 343 MB
- SHA256:
- a69362519c80f27a5924cb5bdcd60f83878b203445547711e77cbf38f88a88f4
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