Image Classification
Transformers
Safetensors
ortho_vit
computer-vision
vit
vision-transformer
orthogonal-residual-updates
imagenet
custom_code
Instructions to use BootsofLagrangian/ortho-vit-b-imagenet1k-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BootsofLagrangian/ortho-vit-b-imagenet1k-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="BootsofLagrangian/ortho-vit-b-imagenet1k-hf", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("BootsofLagrangian/ortho-vit-b-imagenet1k-hf", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 1064c8db19a29e1e1cdf74c3c28e025a982bfcf0c1641ab292da8087b308f08e
- Size of remote file:
- 254 kB
- SHA256:
- ca335c801df063260327af4b4e4ecdba29aceff98168a161ffd2d04507c33de9
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