Image Segmentation
Transformers
Safetensors
PyTorch
segformer
brain-mri
medical
medical-imaging
semantic-segmentation
Eval Results (legacy)
Instructions to use kiselyovd/brain-mri-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kiselyovd/brain-mri-segmentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="kiselyovd/brain-mri-segmentation")# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("kiselyovd/brain-mri-segmentation") model = SegformerForSemanticSegmentation.from_pretrained("kiselyovd/brain-mri-segmentation") - Notebooks
- Google Colab
- Kaggle
| { | |
| "do_normalize": true, | |
| "do_reduce_labels": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_processor_type": "SegformerImageProcessor", | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "reduce_labels": true, | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 512, | |
| "width": 512 | |
| } | |
| } | |