Instructions to use timm/resnet152.a1_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/resnet152.a1_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/resnet152.a1_in1k", pretrained=True) - Transformers
How to use timm/resnet152.a1_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/resnet152.a1_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/resnet152.a1_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/resnet152.a1_in1k: direct link, hf CLI and curl.
- Browser
- Download file 242 MB
-
https://huggingface.co/timm/resnet152.a1_in1k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/resnet152.a1_in1k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/resnet152.a1_in1k/resolve/main/pytorch_model.bin
242 MB
- Xet hash:
- b0653a1c19907a690200c00bf111cdb7eaa39a9e642fce605db6e81c77a85840
- Size of remote file:
- 242 MB
- SHA256:
- 840dddf2a08a196d90a304a653ebbe9cd726197b49afb14e62f587695aedc1f6
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