Instructions to use timm/resnet101.a3_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/resnet101.a3_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/resnet101.a3_in1k", pretrained=True) - Transformers
How to use timm/resnet101.a3_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/resnet101.a3_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.a3_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f4aae8974597a104bfcb41fb7662eeb4f8e735f74ab3141d90e53e95abc630ab
- Size of remote file:
- 179 MB
- SHA256:
- bbedcb4e49d0be15f8ca4a0bbcd6232169560665b1f31ca6311d52a64d057187
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