Instructions to use timm/resnet101.a1h_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/resnet101.a1h_in1k with timm:
import timm model = timm.create_model("hf-hub:timm/resnet101.a1h_in1k", pretrained=True) - Transformers
How to use timm/resnet101.a1h_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/resnet101.a1h_in1k") 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/resnet101.a1h_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/resnet101.a1h_in1k: direct link, hf CLI and curl.
- Browser
- Download file 179 MB
-
https://huggingface.co/timm/resnet101.a1h_in1k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/resnet101.a1h_in1k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/resnet101.a1h_in1k/resolve/main/pytorch_model.bin
179 MB
- Xet hash:
- 8898604a88f76b1733398f4bec2bfb1a2169e1bdffe7ed3e3841cb06a943501a
- Size of remote file:
- 179 MB
- SHA256:
- cbeb27b161047e440a69384be41f4cedc858dbbd74854ad5a9ff81c462554541
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