Instructions to use universalml/gsg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalml/gsg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="universalml/gsg") 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("universalml/gsg") model = AutoModelForImageClassification.from_pretrained("universalml/gsg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6b432719e1ab99f524a00558c03bd081a54dde9946f750e6db2c3023dd0be4da
- Size of remote file:
- 4.98 kB
- SHA256:
- 6f3eb2e16fabd36061ab216b8204985a842a6f6fada460fb56dc309e00a77ab7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.