Instructions to use hf-internal-testing/tiny-random-DeformableDetrForObjectDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DeformableDetrForObjectDetection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="hf-internal-testing/tiny-random-DeformableDetrForObjectDetection")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-DeformableDetrForObjectDetection") model = AutoModelForObjectDetection.from_pretrained("hf-internal-testing/tiny-random-DeformableDetrForObjectDetection") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e7c97331079f5aa3ab3d934f81f06ce4d8765f2b8c243824462a5aabd402ecc
- Size of remote file:
- 123 MB
- SHA256:
- dd3e64e1d5af92aa61954e0f61bb0e6778f7b1402b0330fa04d34a217717f940
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.