Instructions to use OpenGVLab/pvt_v2_b4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/pvt_v2_b4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="OpenGVLab/pvt_v2_b4") 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_b4") model = AutoModelForImageClassification.from_pretrained("OpenGVLab/pvt_v2_b4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7c5cb8d9f81db4f4fcaa6eb41d70ec68db27ec8fe508aae0a5678e88b1e99a6
- Size of remote file:
- 250 MB
- SHA256:
- 8ff81ec569db6a14bd1d3cd134300ef470295e15c68a3d05ea4c0d07df3b1d38
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.