Instructions to use timm/resnetv2_101x3_bit.goog_in21k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/resnetv2_101x3_bit.goog_in21k with timm:
import timm model = timm.create_model("hf-hub:timm/resnetv2_101x3_bit.goog_in21k", pretrained=True) - Transformers
How to use timm/resnetv2_101x3_bit.goog_in21k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/resnetv2_101x3_bit.goog_in21k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("timm/resnetv2_101x3_bit.goog_in21k") model = AutoModelForImageClassification.from_pretrained("timm/resnetv2_101x3_bit.goog_in21k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/resnetv2_101x3_bit.goog_in21k: direct link, hf CLI and curl.
- Browser
- Download file 2.06 GB
-
https://huggingface.co/timm/resnetv2_101x3_bit.goog_in21k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/resnetv2_101x3_bit.goog_in21k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/resnetv2_101x3_bit.goog_in21k/resolve/main/pytorch_model.bin
2.06 GB
- Xet hash:
- 2731a80e12da2458d13a6ad54b41036bbc228592477527116d2381ae52cece76
- Size of remote file:
- 2.06 GB
- SHA256:
- dd952e58e33c06ccc8fabb37cf8b6ad14508401fd90bbac6eaede0618561d04e
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