Instructions to use timm/pit_s_distilled_224.in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/pit_s_distilled_224.in1k with timm:
import timm model = timm.create_model("hf_hub:timm/pit_s_distilled_224.in1k", pretrained=True) - Transformers
How to use timm/pit_s_distilled_224.in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/pit_s_distilled_224.in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/pit_s_distilled_224.in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7616eed1e4dc48e074e31012081b38a3b56ba798dbaded9724e63667f8d79321
- Size of remote file:
- 96.2 MB
- SHA256:
- 5d5be882e81e8ec1933df0406454327636a57f8ef1173b80b55b45b3620b7bb7
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