Instructions to use mwalmsley/zoobot-encoder-euclid-maxvit-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use mwalmsley/zoobot-encoder-euclid-maxvit-base with timm:
import timm model = timm.create_model("hf_hub:mwalmsley/zoobot-encoder-euclid-maxvit-base", pretrained=True) - Transformers
How to use mwalmsley/zoobot-encoder-euclid-maxvit-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mwalmsley/zoobot-encoder-euclid-maxvit-base") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mwalmsley/zoobot-encoder-euclid-maxvit-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da7cb5ab937864e5bca574b3c03f55b2ff09f58f9a169c33493f851382f783c4
- Size of remote file:
- 463 MB
- SHA256:
- 7338b7e55f5664f196a8719697fd8184775cac7298a228cdc7de0ac1cdb88c35
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