Instructions to use OpenGVLab/pvt_v2_b0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/pvt_v2_b0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="OpenGVLab/pvt_v2_b0") 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_b0") model = AutoModelForImageClassification.from_pretrained("OpenGVLab/pvt_v2_b0", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Fixed model name/path in config.
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Update "out_indices" and "out_features" fields in config for compatibility with new code
Verified signatureThese fields were previously being stored incorrectly to the JSON with the underscore prefix (e.g. "_out_indices"). This was an error, and they should be saved here without the underscore prefix as "out_indices" for correct loading.6a7a23d
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Upload folder using huggingface_hub
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initial commit
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