Instructions to use timm/resnet101.a2_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/resnet101.a2_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/resnet101.a2_in1k", pretrained=True) - Transformers
How to use timm/resnet101.a2_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/resnet101.a2_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/resnet101.a2_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b35201ce6e589aef41094664a7af42d4ba2af9294234ca4f6eec83dfc43f19a
- Size of remote file:
- 179 MB
- SHA256:
- afee143882813b437259c3d402f679d0edb5c4931a25544929c5a5f018030e63
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