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
| { | |
| "architectures": [ | |
| "SegformerForSemanticSegmentation" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "classifier_dropout_prob": 0.1, | |
| "decoder_hidden_size": 768, | |
| "depths": [ | |
| 3, | |
| 4, | |
| 6, | |
| 3 | |
| ], | |
| "downsampling_rates": [ | |
| 1, | |
| 4, | |
| 8, | |
| 16 | |
| ], | |
| "drop_path_rate": 0.1, | |
| "dtype": "float32", | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_sizes": [ | |
| 64, | |
| 128, | |
| 320, | |
| 512 | |
| ], | |
| "image_size": 224, | |
| "initializer_range": 0.02, | |
| "layer_norm_eps": 1e-06, | |
| "mlp_ratios": [ | |
| 4, | |
| 4, | |
| 4, | |
| 4 | |
| ], | |
| "model_type": "segformer", | |
| "num_attention_heads": [ | |
| 1, | |
| 2, | |
| 5, | |
| 8 | |
| ], | |
| "num_channels": 3, | |
| "num_encoder_blocks": 4, | |
| "patch_sizes": [ | |
| 7, | |
| 3, | |
| 3, | |
| 3 | |
| ], | |
| "reshape_last_stage": true, | |
| "semantic_loss_ignore_index": 255, | |
| "sr_ratios": [ | |
| 8, | |
| 4, | |
| 2, | |
| 1 | |
| ], | |
| "strides": [ | |
| 4, | |
| 2, | |
| 2, | |
| 2 | |
| ], | |
| "transformers_version": "5.5.4" | |
| } | |