Instructions to use OpenGVLab/pvt_v2_b5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/pvt_v2_b5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="OpenGVLab/pvt_v2_b5") 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_b5") model = AutoModelForImageClassification.from_pretrained("OpenGVLab/pvt_v2_b5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9a1e928b54654833e00759a48717d424431248d95a768e6415f53d57457de40
- Size of remote file:
- 328 MB
- SHA256:
- ccbc55cf99f06303b7bf5c7a34f9774e41143e127f17eedbb922f2d3d1755312
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