Instructions to use shubhamWi91/train56 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shubhamWi91/train56 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="shubhamWi91/train56")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("shubhamWi91/train56") model = AutoModelForObjectDetection.from_pretrained("shubhamWi91/train56") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf86e93d2637f25e2506340df86b1ef2b26934e40d01d7b03560fca2211db50d
- Size of remote file:
- 4.09 kB
- SHA256:
- 6163c25d85e0f641825787939246c7d27228b69f324977065e566238b117cb62
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.