Instructions to use timm/fasternet_l.in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/fasternet_l.in1k with timm:
import timm model = timm.create_model("hf_hub:timm/fasternet_l.in1k", pretrained=True) - Transformers
How to use timm/fasternet_l.in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/fasternet_l.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/fasternet_l.in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 14aec4aa461f61fb98cc4b7e5fbce3063be4bb28a113a3686af036b36d5bb7af
- Size of remote file:
- 374 MB
- SHA256:
- e263b620b37e4a2bb786e8ec393ed76d0747be9c8738ca3abe93f72fce444d23
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