Instructions to use William0609/trained_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use William0609/trained_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="William0609/trained_model")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("William0609/trained_model") model = AutoModelForObjectDetection.from_pretrained("William0609/trained_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88412e9ebce5b2bf7b6dfb7d80d2feaef72b1ccecfed3b2c133350bc720fdc9b
- Size of remote file:
- 167 MB
- SHA256:
- ba077bc90b175a02b1bd42ad094b26bf2b004094ce71b167eec9d10c35a53f45
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.