Update README.md

README.md

@@ -1,7 +1,71 @@
 ---
+license: apache-2.0
 library_name: keras
+language: en
+tags:
+- vision
+- maxim
+- image-to-image
+datasets:
+- sots-outdoor
 ---
+
+# MAXIM pre-trained on RESIDE-Outdoor for image dehazing 
+
+MAXIM model pre-trained for image dehazing. It was introduced in the paper [MAXIM: Multi-Axis MLP for Image Processing](https://arxiv.org/abs/2201.02973) by Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, Yinxiao Li and first released in [this repository](https://github.com/google-research/maxim). 
+
+Disclaimer: The team releasing MAXIM did not write a model card for this model so this model card has been written by the Hugging Face team.
+
 ## Model description
-This is the MAXIM model as described in [MAXIM: Multi-Axis MLP for Image Processing](https://arxiv.org/2201.02973) by Tu et al. The model was obtained by porting the official JAX params available [here](https://github.com/google-research/maxim). Porting code is available [here](https://github.com/sayakpaul/maxim-tf).
+
+MAXIM introduces a shared MLP-based backbone for different image processing tasks such as image deblurring, deraining, denoising, dehazing, low-light image enhancement, and retouching. The following figure depicts the main components of MAXIM:
+
+![](https://github.com/google-research/maxim/raw/main/maxim/images/overview.png)
+
+## Training procedure and results
+
+The authors didn't release the training code. For more details on how the model was trained, refer to the [original paper](https://arxiv.org/abs/2201.02973). 
+
+As per the [table](https://github.com/google-research/maxim#results-and-pre-trained-models), the model achieves a PSNR of 34.19 and an SSIM of 0.985. 
+
 ## Intended uses & limitations
-The model was pre-trained on the Sots outdoor dataset and is intended to use for image dehazing.
+
+You can use the raw model for image dehazing tasks. 
+
+The model is [officially released in JAX](https://github.com/google-research/maxim). It was ported to TensorFlow in [this repository](https://github.com/sayakpaul/maxim-tf). 
+
+### How to use
+
+Here is how to use this model:
+
+```python
+from huggingface_hub import from_pretrained_keras
+from PIL import Image
+
+import tensorflow as tf
+import numpy as np
+import requests
+
+url = https://github.com/sayakpaul/maxim-tf/raw/main/images/Dehazing/input/0048_0.9_0.2.png
+image = Image.open(requests.get(url, stream=True).raw)
+image = np.array(image)
+image = tf.convert_to_tensor(image)
+image = tf.image.resize(image, (256, 256))
+
+model = from_pretrained_keras("google/maxim-s2-dehazing-sots-outdoor")
+predictions = model.predict(tf.expand_dims(image, 0))
+```
+
+For a more elaborate prediction pipeline, refer to [this Colab Notebook](https://colab.research.google.com/github/sayakpaul/maxim-tf/blob/main/notebooks/inference-dynamic-resize.ipynb). 
+
+### Citation
+
+```bibtex
+@article{tu2022maxim,
+  title={MAXIM: Multi-Axis MLP for Image Processing},
+  author={Tu, Zhengzhong and Talebi, Hossein and Zhang, Han and Yang, Feng and Milanfar, Peyman and Bovik, Alan and Li, Yinxiao},
+  journal={CVPR},
+  year={2022},
+}
+```
+