Instructions to use hf-tiny-model-private/tiny-random-DetrForSegmentation 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-DetrForSegmentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="hf-tiny-model-private/tiny-random-DetrForSegmentation")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageSegmentation processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-DetrForSegmentation") model = AutoModelForImageSegmentation.from_pretrained("hf-tiny-model-private/tiny-random-DetrForSegmentation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 70f58debfcab459375cc1e867cd23175a5c9f185b0b5ac546e92b76c07fe1f03
- Size of remote file:
- 109 MB
- SHA256:
- 2c695dcf487735676cd0868f26c70d5e20f07b5df5a2388e0fc5c0afc3249c10
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