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 model name/path
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Added first encoder layer to "out_indices" and "out_features"
Verified signatureThis will lead to better results when using PVTv2 as a backbone with default settings in a model like Deformable DETR.a924444 -
Fixed "out_indices" and "out_features" fields in config
Verified signatureThese fields were previously being stored incorrectly with underscore prefixes as "_out_indices" and "_out_features". They should be stored as "out_indices" and "out_features" without the prefix for proper loading of PVTv2 as a backbone.1b580c9
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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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