added model card and model weights

README.md

@@ -0,0 +1,110 @@
+# XPDNet-brain-af8
+---
+tags:
+- TensorFlow
+- MRI reconstruction
+- MRI
+datasets:
+- fastMRI
+---
+
+This model was used to achieve the 2nd highest submission in terms of PSNR on the fastMRI dataset (see https://fastmri.org/leaderboards/).
+It is a base model for acceleration factor 8.
+The model uses 25 iterations and a medium MWCNN, and a big sensitivity maps refiner.
+
+## Model description
+For more details, see https://arxiv.org/abs/2010.07290.
+This section is WIP.
+
+## Intended uses and limitations
+This model can be used to reconstruct brain data from Siemens scanner at acceleration factor 8.
+It was shown [here](https://arxiv.org/abs/2106.00753), that it can generalize well, although further tests are required.
+
+## How to use
+This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark.
+After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`.
+The framework is TensorFlow.
+
+You can initialize and load the model weights as follows:
+```python
+import tensorflow as tf
+
+from fastmri_recon.models.subclassed_models.denoisers.proposed_params import get_model_specs
+from fastmri_recon.models.subclassed_models.xpdnet import XPDNet
+
+
+n_primal = 5
+model_fun, model_kwargs, n_scales, res = [
+        (model_fun, kwargs, n_scales, res)
+        for m_name, m_size, model_fun, kwargs, _, n_scales, res in get_model_specs(n_primal=n_primal, force_res=False)
+        if m_name == 'MWCNN' and m_size == 'medium'
+][0]
+model_kwargs['use_bias'] = False
+run_params = dict(
+    n_primal=n_primal,
+    multicoil=True,
+    n_scales=n_scales,
+    refine_smaps=True,
+    refine_big=True,
+    res=res,
+    output_shape_spec=True,
+    n_iter=25,
+)
+model = XPDNet(model_fun, model_kwargs, **run_params)
+kspace_size = [1, 1, 320, 320]
+inputs = [
+    tf.zeros(kspace_size + [1], dtype=tf.complex64),  # kspace
+    tf.zeros(kspace_size, dtype=tf.complex64),  # mask
+    tf.zeros(kspace_size, dtype=tf.complex64),  # smaps
+    tf.constant([[320, 320]]),  # shape
+]
+model(inputs)
+model.load_weights('xpdnet_sense_brain__af8_i25_compound_mssim_rf_smb_MWCNNmedium_1601987069-100.h5')
+```
+
+Using the model is then as simple as:
+```python
+model([
+    kspace,  # shape: [n_slices, n_coils, n_rows, n_cols, 1]
+    mask,  # shape: [n_slices, n_coils, n_rows, n_cols]
+    smaps,  # shape: [n_slices, n_coils, n_rows, n_cols]
+    shape,  # shape: [n_slices, 2]
+])
+```
+
+## Limitations and bias
+The limitations and bias of this model have not been properly investigated.
+
+## Training data
+This model was trained using the [fastMRI dataset](https://fastmri.org/dataset/).
+
+## Training procedure
+The training procedure is described in https://arxiv.org/abs/2010.07290.
+This section is WIP.
+
+## Evaluation results
+On the fastMRI validation dataset, the same model with a smaller sensitivity maps refiner gives the following results for 30 validation volumes per contrast:
+
+| Contrast | T1     | T2     | FLAIR  | T1-POST |
+|----------|--------|--------|--------|---------|
+| PSNR     | 38.57  | 37.41  |  36.81  | 38.90   |
+| SSIM     | 0.9348 | 0.9404 |  0.9086 | 0.9517  |
+
+Further results can be seen on the fastMRI leaderboards for the test and challenge dataset: https://fastmri.org/leaderboards/
+
+
+## Bibtex entry
+```
+@inproceedings{Ramzi2020d,
+archivePrefix = {arXiv},
+arxivId = {2010.07290},
+author = {Ramzi, Zaccharie and Ciuciu, Philippe and Starck, Jean-Luc},
+booktitle = {ISMRM},
+eprint = {2010.07290},
+pages = {1--4},
+title = {{XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge}},
+url = {http://arxiv.org/abs/2010.07290},
+year = {2021}
+}
+```
+

xpdnet_sense_brain__af8_i25_compound_mssim_rf_smb_MWCNNmedium_1601987069-100.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bfc48357db9fb59b4cd6c91387ee8e5b7e5dac5ca31169860a4d8e06365dbd07
+size 623781912