Instructions to use Isaacks/test_push with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Isaacks/test_push with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Isaacks/test_push", device_map="auto")# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("Isaacks/test_push") model = SegformerForSemanticSegmentation.from_pretrained("Isaacks/test_push", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 851c7ad435d8e905418355836a8faf8b354a7278653489d44cd33e5dd5544ace
- Size of remote file:
- 14.9 MB
- SHA256:
- 704fb84c545ea0622a25ed54e00c215b6d99160a97be3579d332e82572159b9b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.