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  ---
 
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  library_name: keras
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
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  ## Model description
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- 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).
 
 
 
 
 
 
 
 
 
 
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  ## Intended uses & limitations
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- The model was pre-trained on the Sots outdoor dataset and is intended to use for image dehazing.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ license: apache-2.0
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  library_name: keras
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+ language: en
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+ tags:
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+ - vision
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+ - maxim
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+ - image-to-image
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+ datasets:
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+ - sots-outdoor
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  ---
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+
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+ # MAXIM pre-trained on RESIDE-Outdoor for image dehazing
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+
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+ 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).
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+
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+ 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.
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+
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  ## Model description
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+
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+ 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:
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+
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+ ![](https://github.com/google-research/maxim/raw/main/maxim/images/overview.png)
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+
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+ ## Training procedure and results
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+
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+ 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).
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+
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+ 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.
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+
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  ## Intended uses & limitations
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+
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+ You can use the raw model for image dehazing tasks.
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+
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+ 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).
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+
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+ ### How to use
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+
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+ Here is how to use this model:
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+
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+ ```python
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+ from huggingface_hub import from_pretrained_keras
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+ from PIL import Image
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+
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+ import tensorflow as tf
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+ import numpy as np
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+ import requests
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+
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+ url = https://github.com/sayakpaul/maxim-tf/raw/main/images/Dehazing/input/0048_0.9_0.2.png
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+ image = Image.open(requests.get(url, stream=True).raw)
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+ image = np.array(image)
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+ image = tf.convert_to_tensor(image)
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+ image = tf.image.resize(image, (256, 256))
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+
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+ model = from_pretrained_keras("google/maxim-s2-dehazing-sots-outdoor")
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+ predictions = model.predict(tf.expand_dims(image, 0))
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+ ```
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+
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+ 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).
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+
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+ ### Citation
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+
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+ ```bibtex
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+ @article{tu2022maxim,
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+ title={MAXIM: Multi-Axis MLP for Image Processing},
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+ author={Tu, Zhengzhong and Talebi, Hossein and Zhang, Han and Yang, Feng and Milanfar, Peyman and Bovik, Alan and Li, Yinxiao},
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+ journal={CVPR},
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+ year={2022},
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+ }
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+ ```
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+