Instructions to use timm/aimv2_3b_patch14_224.apple_pt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/aimv2_3b_patch14_224.apple_pt with timm:
import timm model = timm.create_model("hf_hub:timm/aimv2_3b_patch14_224.apple_pt", pretrained=True) - Transformers
How to use timm/aimv2_3b_patch14_224.apple_pt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/aimv2_3b_patch14_224.apple_pt")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/aimv2_3b_patch14_224.apple_pt", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e540111c8dbb529afadfbe538a55251033d44e0f3ac7987ce8e8484a984e8e2e
- Size of remote file:
- 10.9 GB
- SHA256:
- accb4f2308ca4ecf7b711eed05630588cc9d25080b832b6e7fca64d42735cf52
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