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:
- 88ed4a1e0f7bdd016af210163c1a71c9b516744ddcc85fdaae260abd0b705d10
- Size of remote file:
- 14.8 MB
- SHA256:
- c72b11ae84ff0a4af1fa7e21cc4ffbd1c970a6ada59e06fce784d8e4a2c8defd
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