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", device_map="auto")# 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:
- edc2c7ad182b9cc3b85ef22c332f80a57669610b14954f363b8fc4bd39d69abd
- Size of remote file:
- 106 MB
- SHA256:
- 9811ab6cf7fa12ec0e0adab6412e78906d1e4bf079b6630743e27f89939dddc3
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