Instructions to use OpenGVLab/pvt_v2_b3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/pvt_v2_b3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="OpenGVLab/pvt_v2_b3") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("OpenGVLab/pvt_v2_b3") model = AutoModelForImageClassification.from_pretrained("OpenGVLab/pvt_v2_b3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Fixed "out_indices" and "out_features" fields in config (#1)
Verified signature- Fixed "out_indices" and "out_features" fields in config (1b580c943d5b1df9e9786caca4f3ef978ae95cb9) - Added first encoder layer to "out_indices" and "out_features" (a924444d6cfcfc9e8a1ea37b9844fd999054735d) - Fixed model name/path (84df86a2576b8263bad02d48edebb05129ff0311) Co-authored-by: Nate <FoamoftheSea@users.noreply.huggingface.co>9805c8b
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Update README.md
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Upload folder using huggingface_hub
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initial commit
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