Instructions to use whyoke/object_detection_test_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use whyoke/object_detection_test_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="whyoke/object_detection_test_1")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("whyoke/object_detection_test_1") model = AutoModelForObjectDetection.from_pretrained("whyoke/object_detection_test_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f83a13b8699c1b3d597475cf2d460b4f179a302e53fb52f7cd884f7e88e7ae6
- Size of remote file:
- 167 MB
- SHA256:
- f3d41781b2da6eebb9186879b5825c1f6155c089f3b32c0eceb4dacc08afd5ba
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.