added model card and model weights

README.md

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+# UPDNet-knee-af8
+---
+tags:
+- TensorFlow
+- MRI reconstruction
+- MRI
+datasets:
+- fastMRI
+---
+
+This model was used to achieve the 9th highest submission in terms of PSNR on the fastMRI dataset (see https://fastmri.org/leaderboards/) (0.2dB behind the 2nd submission).
+It is a base model for acceleration factor 8.
+The model uses 25 iterations and a medium-ca-prelu U-net, and a medium 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 knee data from Siemens scanner at acceleration factor 8.
+
+## 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.updnet import UPDNet
+
+
+model = UPDNet(
+    multicoil=True,
+    n_dual=1,
+    primal_only=True,
+    n_layers=4,
+    n_iter=25,
+    channel_attention_kwargs={'dense': True},
+    refine_smaps=True,
+    non_linearity='prelu',
+    layers_n_channels=[16 * 2**i for i in range(4)],
+)
+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
+]
+model(inputs)
+model.load_weights('UPDNet-knee-af8/model_weights.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]
+])
+```
+
+## 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
+No evaluation available outside the one from the fastMRI leaderboard (id: `updnet_v3`).
+
+
+## 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}
+}
+```
+

model_weights.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8ae0201a7b9e9013bf748191d0ed9eb6bcca08d614e0dfb70241cd26abe91ad2
+size 50442672