Instructions to use OpenGVLab/pvt_v2_b2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/pvt_v2_b2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="OpenGVLab/pvt_v2_b2") 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") model = AutoModelForImageClassification.from_pretrained("OpenGVLab/pvt_v2_b2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e10d163f0228210675d6e8f9d94ed78b40af73417ce7cc2cde1b6efb026c2cdc
- Size of remote file:
- 101 MB
- SHA256:
- 7de92c6b5d39f1b50f7d4a3f45ec5c18c56f171b370f05c34d70ae977db0b61d
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