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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Update "out_indices" and "out_features" fields in config for compatibility with new code (#1)
Verified signature- Update "out_indices" and "out_features" fields in config for compatibility with new code (6a7a23dc1d221f1f7d3a58ec33e2137a39da0cdf) - Fixed model name/path in config. (25198515fd7daf5f804dce94c087b6f9789f866d) Co-authored-by: Nate <FoamoftheSea@users.noreply.huggingface.co>bafa29f
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