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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 795852663c71c419cbb19b51bed43872fc40bc0d9fe33eb66951691a967924e8
- Size of remote file:
- 90.3 MB
- SHA256:
- 446a8206dea5924c6c59b975a3dc7cc42fe1df81c095fe5308ded479112cad4d
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