Instructions to use shrikant11/segment2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shrikant11/segment2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="shrikant11/segment2") 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("shrikant11/segment2") model = AutoModelForImageClassification.from_pretrained("shrikant11/segment2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cac23b224c86d49b4d0114ad85ac1091d796d554deb7dfdbd459f011ad5096e1
- Size of remote file:
- 339 MB
- SHA256:
- 97152732a718e09f643411bcb09899bf07373ea266ba98b8ac699cd389538c56
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.