| { |
| "model_name": "mito_aff_unet_setup_16", |
| "model_type": "UNet", |
| "framework": "torch", |
| "spatial_dims": 3, |
| "in_channels": 1, |
| "out_channels": 3, |
| "iteration": 400000, |
| "input_voxel_size": [ |
| 16, |
| 16, |
| 16 |
| ], |
| "output_voxel_size": [ |
| 16, |
| 16, |
| 16 |
| ], |
| "channels_names": [ |
| "mito_aff_1", |
| "mito_aff_2", |
| "mito_aff_3" |
| ], |
| "input_shape": [ |
| 178, |
| 178, |
| 178 |
| ], |
| "output_shape": [ |
| 56, |
| 56, |
| 56 |
| ], |
| "inference_input_shape": [ |
| 378, |
| 378, |
| 378 |
| ], |
| "inference_output_shape": [ |
| 256, |
| 256, |
| 256 |
| ], |
| "author": "Marwan Zouinkhi", |
| "description": "Generated affinities for mitochondria segmentation using a UNet architecture trained on setup 16 with 400k iterations.", |
| "version": "1.0.0", |
| "format_version": "1" |
| } |