Instructions to use mwalmsley/zoobot-encoder-evo-maxvit-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use mwalmsley/zoobot-encoder-evo-maxvit-base with timm:
import timm model = timm.create_model("hf_hub:mwalmsley/zoobot-encoder-evo-maxvit-base", pretrained=True) - Transformers
How to use mwalmsley/zoobot-encoder-evo-maxvit-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mwalmsley/zoobot-encoder-evo-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-evo-maxvit-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57f7c117a26ca249d37a53aa8f991ce192a43aecdf1c96ed104b08f9a12a9e21
- Size of remote file:
- 463 MB
- SHA256:
- fd97d960108f271f36922988602f9a8307e1d572db0d8c88b6c48dc7b516dbac
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