Instructions to use omarelsayeed/t with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omarelsayeed/t with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="omarelsayeed/t")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("omarelsayeed/t") model = AutoModelForObjectDetection.from_pretrained("omarelsayeed/t", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from omarelsayeed/t: direct link, hf CLI and curl.
- Browser
- Download file 165 MB
-
https://huggingface.co/omarelsayeed/t/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://omarelsayeed/t/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/omarelsayeed/t/resolve/main/pytorch_model.bin
165 MB
- Xet hash:
- 346185ed3c7cd8fa96aaa08a345cf6df4395e1f806c12343b2b9b47e84b72112
- Size of remote file:
- 165 MB
- SHA256:
- 6da95cd0d7eec6a1da18871c9c137280c9cb5aa50c1bf23a694d8b4bfa0843e9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.