Instructions to use OpenGVLab/pvt_v2_b4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/pvt_v2_b4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="OpenGVLab/pvt_v2_b4") 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_b4") model = AutoModelForImageClassification.from_pretrained("OpenGVLab/pvt_v2_b4", 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 (8e50da03c79ffa19c00f6f72e99b6cc0769d944e) - Added first encoder layer to "out_indices" and "out_features" (737b0d31a055a0fc201fd9e6588a80a3dc919311) - Fixed model name/path (6cec28e9dc5cb44625dfe3c57b5ddba8dd6e0393) Co-authored-by: Nate <FoamoftheSea@users.noreply.huggingface.co>9d46746
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Update README.md
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