Instructions to use timm/gcresnet33ts.ra2_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/gcresnet33ts.ra2_in1k with timm:
import timm model = timm.create_model("hf-hub:timm/gcresnet33ts.ra2_in1k", pretrained=True) - Transformers
How to use timm/gcresnet33ts.ra2_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/gcresnet33ts.ra2_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/gcresnet33ts.ra2_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/gcresnet33ts.ra2_in1k: direct link, hf CLI and curl.
- Browser
- Download file 79.8 MB
-
https://huggingface.co/timm/gcresnet33ts.ra2_in1k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/gcresnet33ts.ra2_in1k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/gcresnet33ts.ra2_in1k/resolve/main/pytorch_model.bin
79.8 MB
- Xet hash:
- bae9cc33de7d0f87aead141d0182d17b9fa047caeb9946e2a8cf7394fda63894
- Size of remote file:
- 79.8 MB
- SHA256:
- 3c68bb352c949f9b7d5cd96f4aede7fc9b26ade6ad9f4446c5d60b81e6423f83
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