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This repository provides a guideline and code to convert your pre-trained models into an appropriate submission format forAI Challenge organized in conjunction with ECCV 2018 Conference.

• TensorFlow (>=1.8)
• Python (3.5+) packages:scipy,numpy,psutil
  
  

• Your model should be able to process images ofarbitrary resolution
• It should require no more than3.5GB of RAM while processing HD-resolution[1280x720px] photos
• Maximum model size:100MB
• Should be saved asTensorflow .pb file
  
  

Your model should be implemented as a function that takes one single input and produces one output:

• Input:  Tensorflow 4-dimensional tensor of shape[batch_size, image_height, image_width, 3]
  _{In the Super-Resolution task, input images are alreadybicubically interpolated (x4) and have the same size as target high-resolution photos}
  
• Output:  Same as input
• Values:  The values of both original and processed images should lie in theinterval [0, 1]

Here is a valid SRCNN function provided for you as a reference:

def srcnn(images):    with tf.variable_scope("generator"):        weights = {'w1': tf.Variable(tf.random_normal([9, 9, 3, 64], stddev=1e-3), name='w1'),'w2': tf.Variable(tf.random_normal([5, 5, 64, 32], stddev=1e-3), name='w2'),'w3': tf.Variable(tf.random_normal([5, 5, 32, 3], stddev=1e-3), name='w3')        }        biases = {'b1': tf.Variable(tf.zeros([64]), name='b1'),'b2': tf.Variable(tf.zeros([32]), name='b2'),'b3': tf.Variable(tf.zeros([1]), name='b3')        }        conv1 = tf.nn.relu(tf.nn.conv2d(images, weights['w1'], strides=[1,1,1,1], padding='SAME') + biases['b1'])        conv2 = tf.nn.relu(tf.nn.conv2d(conv1, weights['w2'], strides=[1,1,1,1], padding='SAME') + biases['b2'])        conv3 = tf.nn.conv2d(conv2, weights['w3'], strides=[1,1,1,1], padding='SAME') + biases['b3']return tf.nn.tanh(conv3)* 0.58 + 0.5

To test and convert your pre-trained models, run the following scripts:

• Track A, Image Super-Resolution: evaluate_super_resolution.py
• Track B, Image Enhancement: evaluate_enhancement.py

You need to modify two lines in the headers of the above scripts:

from<model_file> import<your_model> as test_modelmodel_location ="path/to/your/saved/pre-trained/model"

Here,model_file.py should be a python file containing your model definition,your_model is the actual function that defines your model, andmodel_location points to your saved pre-trained model file.

After running these scripts, they will:

1. save your model asmodel.pb file stored inmodels_pretrained/ folder
2. compute PSNR/SSIM scores on a subset of validation images/patches
3. compute running time andestimated RAM consumption for HD-resolution images
   
   

Apart from the validation scripts, we also provide you several pre-trained models that can be restored and validated using the same scripts. In all cases, model architectures are defined in themodels.py file.

Super-resolution task:

1. SRCNN, function:srcnn, pre-trained model:models_pretrained/div2k_srcnn
2. ResNet with one residual block, function:resnet_6_16, pre-trained model:models_pretrained/div2k_resnet_6_16
3. VGG-19, function:vgg_19, pre-trained model:models_pretrained/div2k_vgg19_vdsr.ckpt

Image Enhancement task:

1. SRCNN, function:srcnn, pre-trained model:models_pretrained/dped_srcnn
2. ResNet with 4 residual blocks, function:resnet_12_64, pre-trained model:models_pretrained/dped_resnet_12_64
3. ResNet with 2 residual blocks, function:resnet_8_32, pre-trained model:models_pretrained/dped_resnet_8_32
   
   

To register your team, send an email toai.mobile.challenge@gmail.com with the following information:

Email Subject:  AI Mobile Challenge RegistrationEmail Text:     Team Name                Team Member 1 (Name, Surname, Affiliation)                Team Member 2 (Name, Surname, Affiliation)                ....

To validate your model, send an email indicating thetrack,team id and the correspondingmodel.pb file:

Email Subject:  [Track X] [Team ID] [Team Name] SubmissionEmail Text:     Link to model.pb file

You are allowed to send up to2 submissions per day for each track. The leaderboard will show the results of your last successful submission. Please make sure that the results provided by our validation scripts are meaningful before sending your submission files.

The performance of your solution will be assessed based on three metrics: its speed compared to a baseline network, its fidelity score measured by PSNR, and its perceptual score computed based on MS-SSIM metric. Since PSNR and SSIM scores do not always objectively reflect image quality, during the test phase we will conduct a user study where your final submissions will be evaluated by a large number of people, and the resulting MOS Scores will replace MS-SSIM results. The total score of your solution will be calculated as a weighted average of the previous scores:

TotalScore = α* (PSNR_solution - PSNR_baseline) + β* (SSIM_solution - SSIM_baseline) + γ* min(Time_baseline / Time_solution, 4)

We will use three different validation tracks for evaluating your results. Score A is giving preference to solution with the highest fidelity (PSNR) score, score B is aimed at the solution providing the best visual results (MS-SSIM/MOS scores), and score C is targeted at the best balance between the speed and perceptual/quantitative performance. For each track, we will use the above scoring formula but with different coefficients:

Track A (Super-Resolution):

• PSNR_baseline = 26.5,SSIM_baseline = 0.94
• (α,β,γ):   score A - (4, 100, 1);   score B - (1, 400, 1);   score C - (2, 200, 1.5)

Track B (Image Enhancement):

• PSNR_baseline = 21,SSIM_baseline = 0.9
• (α,β,γ):   score A - (4, 100, 2);   score B - (1, 400, 2);   score C - (2, 200, 2.9)
  
  

• Note that the provided code is used only for preliminary model validation, while all final numbers will be obtained by us by testing all submissions on the test parts of the datasets (accuracy) and on the same hardware (speed)

• To check the above RAM requirements, we will run your submissions on a GPU with 3.5GB of RAM.
  In case this won't be enough for your model, it will be disqualified from the final validation stage