Instructions to use timm/tinynet_c.in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/tinynet_c.in1k with timm:
import timm model = timm.create_model("hf_hub:timm/tinynet_c.in1k", pretrained=True) - Transformers
How to use timm/tinynet_c.in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/tinynet_c.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/tinynet_c.in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc81fd2a9f858f150e83376221bbb70747cd29abb3a6703c1ea8caa1d2a854b6
- Size of remote file:
- 10 MB
- SHA256:
- ead42318852042fd0a317998fb305dd614d000d018d4c305b70ac37bb61780a9
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