Instructions to use hf-tiny-model-private/tiny-random-ConditionalDetrForObjectDetection 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-ConditionalDetrForObjectDetection 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-ConditionalDetrForObjectDetection")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-ConditionalDetrForObjectDetection") model = AutoModelForObjectDetection.from_pretrained("hf-tiny-model-private/tiny-random-ConditionalDetrForObjectDetection") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 271e30b89f9b3e29c158de3c67127de10bfa28b6209678bb1aee35e5598f494b
- Size of remote file:
- 107 MB
- SHA256:
- 34d04b281d1d90a977599531c3fcc8a1f05399a8ebb62ada6213a4dc61d59e40
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.