Instructions to use hf-tiny-model-private/tiny-random-YolosForObjectDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-YolosForObjectDetection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="hf-tiny-model-private/tiny-random-YolosForObjectDetection")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-YolosForObjectDetection") model = AutoModelForObjectDetection.from_pretrained("hf-tiny-model-private/tiny-random-YolosForObjectDetection") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c6d19c403af7537f8c68eaa2a26cd7d92b7182a4d194be51d3d191e947800261
- Size of remote file:
- 319 kB
- SHA256:
- 947cf675c515ed908fbefbd0eba1e55456dfb33bb3b07c76ecaa911a7faf9857
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.