Instructions to use timm/vit_betwixt_patch16_reg4_gap_256.sbb_in12k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_betwixt_patch16_reg4_gap_256.sbb_in12k with timm:
import timm model = timm.create_model("hf-hub:timm/vit_betwixt_patch16_reg4_gap_256.sbb_in12k", pretrained=True) - Transformers
How to use timm/vit_betwixt_patch16_reg4_gap_256.sbb_in12k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/vit_betwixt_patch16_reg4_gap_256.sbb_in12k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_betwixt_patch16_reg4_gap_256.sbb_in12k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/vit_betwixt_patch16_reg4_gap_256.sbb_in12k: direct link, hf CLI and curl.
- Browser
- Download file 269 MB
-
https://huggingface.co/timm/vit_betwixt_patch16_reg4_gap_256.sbb_in12k/resolve/02e3fb4eb9436bfd2cc8cc63d2f6788ebc8cc096/pytorch_model.bin
- Command line
-
hf download hf://timm/vit_betwixt_patch16_reg4_gap_256.sbb_in12k@02e3fb4eb9436bfd2cc8cc63d2f6788ebc8cc096/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/vit_betwixt_patch16_reg4_gap_256.sbb_in12k/resolve/02e3fb4eb9436bfd2cc8cc63d2f6788ebc8cc096/pytorch_model.bin
269 MB
- Xet hash:
- c5f91e228aff978ce7223064eaf8516146f09b371f5c08ed9b4bca26c9dc98eb
- Size of remote file:
- 269 MB
- SHA256:
- fb927babfd83cfd08afbca78e6fc6d34b08709e66fdfb279e4218ddcce140098
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.