added model card and model weights

README.md

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+# KIKI-net-fastmri
+---
+tags:
+- TensorFlow
+- MRI reconstruction
+- MRI
+datasets:
+- fastMRI
+---
+
+This model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4.
+
+## Model description
+For more details, see https://www.mdpi.com/2076-3417/10/5/1816.
+This section is WIP.
+
+## Intended uses and limitations
+This model can be used to reconstruct single coil knee data from Siemens scanner at acceleration factor 4.
+It cannot be used on multi-coil data.
+
+## 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
+from fastmri_recon.models.functional_models.kiki_sep import full_kiki_net
+from fastmri_recon.models.utils.non_linearities import lrelu
+
+model = full_kiki_net(n_convs=16, n_filters=48, activation=lrelu)
+model.load_weights('model_weights.h5')
+```
+
+Using the model is then as simple as:
+```python
+model([
+    kspace,  # shape: [n_slices, n_rows, n_cols, 1]
+    mask,  # shape: [n_slices, 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://www.mdpi.com/2076-3417/10/5/1816 for brain data.
+This section is WIP.
+
+## Evaluation results
+This model was evaluated using the [fastMRI dataset](https://fastmri.org/dataset/).
+
+| Contrast | PD    | PDFS   |
+|----------|-------|--------|
+| PSNR     | 32.86  | 29.57  |
+| SSIM     | 0.797 | 0.6271 |
+
+
+## Bibtex entry
+```
+@article{ramzi2020benchmarking,
+  title={Benchmarking MRI reconstruction neural networks on large public datasets},
+  author={Ramzi, Zaccharie and Ciuciu, Philippe and Starck, Jean-Luc},
+  journal={Applied Sciences},
+  volume={10},
+  number={5},
+  pages={1816},
+  year={2020},
+  publisher={Multidisciplinary Digital Publishing Institute}
+}
+```
+

model_weights.h5

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