Instructions to use hf-internal-testing/tiny-random-ConditionalDetrModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ConditionalDetrModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-ConditionalDetrModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-ConditionalDetrModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-ConditionalDetrModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2fe84b22384418e818be76e3eee733dcf8393d4631fb165e458995143ee8a373
- Size of remote file:
- 106 MB
- SHA256:
- bc3107da1f378ec5becedde9e29703ef61f8a3028b699794a6a1a4d9943aa08e
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