Instructions to use 25khattab/vit_test_1_95 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 25khattab/vit_test_1_95 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="25khattab/vit_test_1_95") 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("25khattab/vit_test_1_95") model = AutoModelForImageClassification.from_pretrained("25khattab/vit_test_1_95", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from 25khattab/vit_test_1_95: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/25khattab/vit_test_1_95/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://25khattab/vit_test_1_95/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/25khattab/vit_test_1_95/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 890bd8e41a1b5c10ba696e8b26b7b6a120e4e29e08b8d4f35a40550c35d52155
- Size of remote file:
- 343 MB
- SHA256:
- 6351fb0c28bd3554f4f2f58be2788faa54d182e3525406fc3f9defbdb59c6df7
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