Image Segmentation
Transformers
Safetensors
English
mask2former
Electron-Microscopy
Ultrastructure-Segmentation
Semantic-Segmentation
Instructions to use Dnq2025/MicroStructFormer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dnq2025/MicroStructFormer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Dnq2025/MicroStructFormer")# Load model directly from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation processor = AutoImageProcessor.from_pretrained("Dnq2025/MicroStructFormer") model = Mask2FormerForUniversalSegmentation.from_pretrained("Dnq2025/MicroStructFormer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 27421143da6f43ac3bdc53e8b0c47a58d7677fece3b78111b430147c40481861
- Size of remote file:
- 866 MB
- SHA256:
- c5c4fa498b4cc7cc1af0884394a225de4c0328465cc2171c83082dc1c838731f
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