Instructions to use OpenGVLab/pvt_v2_b2_linear with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/pvt_v2_b2_linear with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="OpenGVLab/pvt_v2_b2_linear") 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_b2_linear") model = AutoModelForImageClassification.from_pretrained("OpenGVLab/pvt_v2_b2_linear", 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 (b7a11872878f1c5a8fbca163f945ed7b11f379d7) - Added first encoder layer to "out_indices" and "out_features". (ab3e8f0ef96252eea16c716f9c4d655794a5dcd4) - Fixed model name/path in config. (44f59b566c5deea3f0aab133c3574b30b9e20057) Co-authored-by: Nate <FoamoftheSea@users.noreply.huggingface.co>4d0cf53
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Update README.md
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