Instructions to use mwalmsley/zoobot-encoder-v3-vit-nano-p8-b32-partial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use mwalmsley/zoobot-encoder-v3-vit-nano-p8-b32-partial with timm:
import timm model = timm.create_model("hf_hub:mwalmsley/zoobot-encoder-v3-vit-nano-p8-b32-partial", pretrained=True) - Transformers
How to use mwalmsley/zoobot-encoder-v3-vit-nano-p8-b32-partial with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mwalmsley/zoobot-encoder-v3-vit-nano-p8-b32-partial") 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-v3-vit-nano-p8-b32-partial", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54ecc383ab51810102f8807779652fec120c0b53722861fa6478cca5bbd3564f
- Size of remote file:
- 6.82 MB
- SHA256:
- 8821ac24ef595335b89c81ccf718008895063d3a203df13383438e5073f98471
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