Instructions to use tue-mps/ade20k_semantic_eomt_large_512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tue-mps/ade20k_semantic_eomt_large_512 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="tue-mps/ade20k_semantic_eomt_large_512")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, EomtForUniversalSegmentation processor = AutoImageProcessor.from_pretrained("tue-mps/ade20k_semantic_eomt_large_512") model = EomtForUniversalSegmentation.from_pretrained("tue-mps/ade20k_semantic_eomt_large_512", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tue-mps/ade20k_semantic_eomt_large_512: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/tue-mps/ade20k_semantic_eomt_large_512/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tue-mps/ade20k_semantic_eomt_large_512/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tue-mps/ade20k_semantic_eomt_large_512/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- d8309d405b3a121be72aa4560c0302d7d4390196e62d42e315ea7270c0afb1af
- Size of remote file:
- 1.26 GB
- SHA256:
- 314968b0cde91d3c85111041aeea4534abaa8ccc50a498f26db89e6469fcbb61
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