Create README.md

README.md

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+---
+language:
+- en
+license: apache-2.0
+library_name: atommic
+datasets:
+- BraTS2023AdultGlioma
+thumbnail: null
+tags:
+- image-segmentation
+- AttentionUNet
+- ATOMMIC
+- pytorch
+model-index:
+- name: SEG_AttentionUNet_BraTS2023AdultGlioma
+  results: []
+
+---
+
+
+## Model Overview
+
+AttentionUNet for MRI Segmentation on the BraTS2023AdultGlioma dataset.
+
+
+## ATOMMIC: Training
+
+To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version.
+```
+pip install atommic['all']
+```
+
+## How to Use this Model
+
+The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
+
+Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/SEG/BraTS2023AdultGlioma/conf).
+
+### Automatically instantiate the model
+
+```base
+pretrained: true
+checkpoint: https://huggingface.co/wdika/SEG_AttentionUNet_BraTS2023AdultGlioma/blob/main/SEG_AttentionUNet_BraTS2023AdultGlioma.atommic
+mode: test
+```
+
+### Usage
+
+You need to download the BraTS 2023 Adult Glioma dataset to effectively use this model. Check the [BraTS2023AdultGlioma](https://github.com/wdika/atommic/blob/main/projects/SEG/BraTS2023AdultGlioma/README.md) page for more information.
+
+
+## Model Architecture
+```base
+model:
+  model_name: SEGMENTATIONATTENTIONUNET
+  segmentation_module: AttentionUNet
+  segmentation_module_input_channels: 4
+  segmentation_module_output_channels: 4
+  segmentation_module_channels: 32
+  segmentation_module_pooling_layers: 5
+  segmentation_module_dropout: 0.0
+  segmentation_module_normalize: false
+  segmentation_module_norm_groups: 2
+  segmentation_loss:
+    dice: 1.0
+  dice_loss_include_background: true  # always set to true if the background is removed
+  dice_loss_to_onehot_y: false
+  dice_loss_sigmoid: false
+  dice_loss_softmax: false
+  dice_loss_other_act: none
+  dice_loss_squared_pred: false
+  dice_loss_jaccard: false
+  dice_loss_flatten: false
+  dice_loss_reduction: mean_batch
+  dice_loss_smooth_nr: 1e-5
+  dice_loss_smooth_dr: 1e-5
+  dice_loss_batch: true
+  dice_metric_include_background: true  # always set to true if the background is removed
+  dice_metric_to_onehot_y: false
+  dice_metric_sigmoid: false
+  dice_metric_softmax: false
+  dice_metric_other_act: none
+  dice_metric_squared_pred: false
+  dice_metric_jaccard: false
+  dice_metric_flatten: false
+  dice_metric_reduction: mean_batch
+  dice_metric_smooth_nr: 1e-5
+  dice_metric_smooth_dr: 1e-5
+  dice_metric_batch: true
+  segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+  segmentation_activation: sigmoid
+  magnitude_input: true
+  log_multiple_modalities: true  # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+  normalization_type: minmax
+  normalize_segmentation_output: true
+  complex_data: false
+```
+
+## Training
+```base
+  optim:
+    name: adam
+    lr: 1e-4
+    betas:
+      - 0.9
+      - 0.98
+    weight_decay: 0.0
+    sched:
+      name: InverseSquareRootAnnealing
+      min_lr: 0.0
+      last_epoch: -1
+      warmup_ratio: 0.1
+
+trainer:
+  strategy: ddp
+  accelerator: gpu
+  devices: 1
+  num_nodes: 1
+  max_epochs: 10
+  precision: 16-mixed
+  enable_checkpointing: false
+  logger: false
+  log_every_n_steps: 50
+  check_val_every_n_epoch: -1
+  max_steps: -1
+```
+
+## Performance
+
+Evaluation can be performed using the segmentation [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/segmentation.py) script for the segmentation task, with --evaluation_type per_slice.
+
+Results
+-------
+
+Evaluation
+----------
+DICE = 0.9305 +/- 0.1257 F1 = 0.6481 +/- 0.7629 HD95 = 3.836 +/- 3.01 IOU = 0.5374 +/- 0.6617
+
+
+## Limitations
+
+This model was trained on the BraTS2023AdultGlioma dataset with stacked T1c, T1n, T2f, T2w images and might differ in performance compared to the leaderboard results.
+
+
+## References
+
+[1] [ATOMMIC](https://github.com/wdika/atommic)
+
+[2] Kazerooni AF, Khalili N, Liu X, et al. The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs). 2023