Instructions to use timm/pvt_v2_b3.in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/pvt_v2_b3.in1k with timm:
import timm model = timm.create_model("hf_hub:timm/pvt_v2_b3.in1k", pretrained=True) - Transformers
How to use timm/pvt_v2_b3.in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/pvt_v2_b3.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/pvt_v2_b3.in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb69e13f3d869ec30f439b692e4f020d7568a13cf93c34f6f04eb73aee9d94fc
- Size of remote file:
- 181 MB
- SHA256:
- 2d4cba0c6c2bc68c7f6dd6b493aad704ddfcf63ac92657dd41f832c3830c8ccc
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