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:
- ed340538d7f005aa5fca6d5345d5495cf4fcc104ae2879e6f1b25a194038ef47
- Size of remote file:
- 16.7 MB
- SHA256:
- 76ed844d056b81b24370eb113d2875a0bfcca68e206763fbbd4f489774cd8333
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