Instructions to use OpenGVLab/pvt_v2_b1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/pvt_v2_b1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="OpenGVLab/pvt_v2_b1") 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_b1") model = AutoModelForImageClassification.from_pretrained("OpenGVLab/pvt_v2_b1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 303235e010f785b9131450165bd24cfc605e8605c309734fa1d2f26b55d8db15
- Size of remote file:
- 56.1 MB
- SHA256:
- ce664e249b8d37bff2b212d7a32c29bff7ff8b6ea01f99ddb830b5465b347c5d
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