Create README.md

README.md

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+---
+language:
+- en
+license: apache-2.0
+library_name: atommic
+datasets:
+- StanfordKnees2019
+thumbnail: null
+tags:
+- image-reconstruction
+- LPDNet
+- ATOMMIC
+- pytorch
+model-index:
+- name: REC_LPDNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM
+  results: []
+
+---
+
+
+## Model Overview
+
+Learned Primal Dual Network (LPDNet) for 12x accelerated MRI Reconstruction on the StanfordKnees2019 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/REC/StanfordKnees2019/conf).
+
+
+### Automatically instantiate the model
+
+```base
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_LPDNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_LPDNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM.atommic
+mode: test
+```
+
+### Usage
+
+You need to download the Stanford Knees 2019 dataset to effectively use this model. Check the [StanfordKnees2019](https://github.com/wdika/atommic/blob/main/projects/REC/StanfordKnees2019/README.md) page for more information.
+
+
+## Model Architecture
+```base
+model:
+  model_name: LPDNet
+  num_primal: 5
+  num_dual: 5
+  num_iter: 5
+  primal_model_architecture: UNET
+  primal_in_channels: 2
+  primal_out_channels: 2
+  primal_unet_num_filters: 16
+  primal_unet_num_pool_layers: 2
+  primal_unet_dropout_probability: 0.0
+  primal_unet_padding_size: 11
+  primal_unet_normalize: true
+  dual_model_architecture: UNET
+  dual_in_channels: 2
+  dual_out_channels: 2
+  dual_unet_num_filters: 16
+  dual_unet_num_pool_layers: 2
+  dual_unet_dropout_probability: 0.0
+  dual_unet_padding_size: 11
+  dual_unet_normalize: true
+  dimensionality: 2
+  reconstruction_loss:
+    wasserstein: 1.0
+```
+
+## Training
+```base
+  optim:
+    name: adamw
+    lr: 1e-4
+    betas:
+      - 0.9
+      - 0.999
+    weight_decay: 0.0
+    sched:
+        name: InverseSquareRootAnnealing
+        min_lr: 0.0
+        last_epoch: -1
+        warmup_ratio: 0.1
+
+trainer:
+  strategy: ddp_find_unused_parameters_false
+  accelerator: gpu
+  devices: 1
+  num_nodes: 1
+  max_epochs: 20
+  precision: 16-mixed
+  enable_checkpointing: false
+  logger: false
+  log_every_n_steps: 50
+  check_val_every_n_epoch: -1
+  max_steps: -1
+```
+
+## Performance
+
+To compute the targets using the raw k-space and the chosen coil combination method, accompanied with the chosen coil sensitivity maps estimation method, you can use [targets](https://github.com/wdika/atommic/tree/main/projects/REC/StanfordKnees2019/conf/targets) configuration files.
+
+Evaluation can be performed using the [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/reconstruction.py) script for the reconstruction task, with --evaluation_type per_slice.
+
+Results
+-------
+
+Evaluation against SENSE targets
+--------------------------------
+12x: MSE = 0.0016 +/- 0.005712 NMSE = 0.05706 +/- 0.09372 PSNR = 29.79 +/- 6.276 SSIM = 0.7366 +/- 0.297
+
+
+
+## Limitations
+
+This model was trained on the StanfordKnees2019 batch0 using a UNet coil sensitivity maps estimation and Geometric Decomposition Coil-Compressions to 1-coil, and might differ from the results reported on the challenge leaderboard.
+
+
+## References
+
+[1] [ATOMMIC](https://github.com/wdika/atommic)
+
+[2] Epperson K, Rt R, Sawyer AM, et al. Creation of Fully Sampled MR Data Repository for Compressed SENSEing of the Knee. SMRT Conference 2013;2013:1