Instructions to use hf-tiny-model-private/tiny-random-ConditionalDetrModel 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-ConditionalDetrModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-tiny-model-private/tiny-random-ConditionalDetrModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-ConditionalDetrModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-ConditionalDetrModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 02b9f19cd0dcd2d9fd8d0be02e61993dd7ca733f2f68d122a4006dbf351819ef
- Size of remote file:
- 106 MB
- SHA256:
- 48a44f7214f9897723efa6db3630b2e678126f83e44097c3bbbbc54fc82386d3
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