Instructions to use timm/fbnetv3_g.ra2_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/fbnetv3_g.ra2_in1k with timm:
import timm model = timm.create_model("hf-hub:timm/fbnetv3_g.ra2_in1k", pretrained=True) - Transformers
How to use timm/fbnetv3_g.ra2_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/fbnetv3_g.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 AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("timm/fbnetv3_g.ra2_in1k") model = AutoModelForImageClassification.from_pretrained("timm/fbnetv3_g.ra2_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/fbnetv3_g.ra2_in1k: direct link, hf CLI and curl.
- Browser
- Download file 67.1 MB
-
https://huggingface.co/timm/fbnetv3_g.ra2_in1k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/fbnetv3_g.ra2_in1k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/fbnetv3_g.ra2_in1k/resolve/main/pytorch_model.bin
67.1 MB
- Xet hash:
- b4f3bdf50fc26cf4d5e91e999141fe55ed2147db84ac72ab82f046574536857c
- Size of remote file:
- 67.1 MB
- SHA256:
- 22d3167b3170ac55a00180100497f18805000aeda60ae064f45513cd7c076721
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