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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- caf0e7fb3f4e2274d8b8dceab1d27f596ffda7a404816cd65239ea9084f51713
- Size of remote file:
- 109 MB
- SHA256:
- 42256022458c8016dfd9b504e74226de92a0873a6be394f6e981bdf64c80e1af
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