Instructions to use mwalmsley/euclid_encoder_mae_zoobot_vit_small_patch8_224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use mwalmsley/euclid_encoder_mae_zoobot_vit_small_patch8_224 with timm:
import timm model = timm.create_model("hf-hub:mwalmsley/euclid_encoder_mae_zoobot_vit_small_patch8_224", pretrained=True) - Transformers
How to use mwalmsley/euclid_encoder_mae_zoobot_vit_small_patch8_224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mwalmsley/euclid_encoder_mae_zoobot_vit_small_patch8_224") 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("mwalmsley/euclid_encoder_mae_zoobot_vit_small_patch8_224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mwalmsley/euclid_encoder_mae_zoobot_vit_small_patch8_224: direct link, hf CLI and curl.
- Browser
- Download file 86.7 MB
-
https://huggingface.co/mwalmsley/euclid_encoder_mae_zoobot_vit_small_patch8_224/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mwalmsley/euclid_encoder_mae_zoobot_vit_small_patch8_224/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mwalmsley/euclid_encoder_mae_zoobot_vit_small_patch8_224/resolve/main/pytorch_model.bin
86.7 MB
- Xet hash:
- fcb92ab21ec784e90d57026e955d46ec4438f218ee0a7b0e205964901f16e082
- Size of remote file:
- 86.7 MB
- SHA256:
- 4a36cec6e18eeaedf193b90976e2a32d17012047ffe67d18486fa53ba05310b8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.