Instructions to use timm/convnext_small.in12k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/convnext_small.in12k with timm:
import timm model = timm.create_model("hf-hub:timm/convnext_small.in12k", pretrained=True) - Transformers
How to use timm/convnext_small.in12k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/convnext_small.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/convnext_small.in12k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/convnext_small.in12k: direct link, hf CLI and curl.
- Browser
- Download file 234 MB
-
https://huggingface.co/timm/convnext_small.in12k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/convnext_small.in12k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/convnext_small.in12k/resolve/main/pytorch_model.bin
234 MB
- Xet hash:
- 166450007f2e98fec5aad764a46bf338124e925ea3f7f6a4f7b9047f29dfee50
- Size of remote file:
- 234 MB
- SHA256:
- 38c29ecedc089f284ba6960483a34907bef604d7ac76ffa4f1aa7b8d443026eb
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