Instructions to use OpenGVLab/pvt_v2_b3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/pvt_v2_b3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="OpenGVLab/pvt_v2_b3") 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_b3") model = AutoModelForImageClassification.from_pretrained("OpenGVLab/pvt_v2_b3") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6396cb2c54c9a10821d5589bcba3f30e4d8eaec37cbcac1e6f6b0b24702154d5
- Size of remote file:
- 181 MB
- SHA256:
- 5aa3abb052da631cf44ae71ee5996da1280be694c483597f58bc3b9a1a4ecfd2
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