Instructions to use timm/fbnetv3_b.ra2_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/fbnetv3_b.ra2_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/fbnetv3_b.ra2_in1k", pretrained=True) - Transformers
How to use timm/fbnetv3_b.ra2_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/fbnetv3_b.ra2_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/fbnetv3_b.ra2_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0c545f06ea66cb376618f4a5087774fbec3da0b8b862a10059711caba6fee66
- Size of remote file:
- 34.8 MB
- SHA256:
- 9e29b6bb0d099175025ffb1e451e07f0021668df90beb3b557142c59e61e1a57
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