The 1st edition of MAI: Mobile AI workshop will be held on June, 2021 in conjunction with CVPR 2021.
Video super-resolution is one of the key computer vision tasks, restoring high-resolution image frames from their low-resolution. Recently, deep convolutional neural networks (DCNNs) have achieved great success in the accurate image/video super-resolution. However, the heavy computation costs of DCNNs limit their applications on mobile devices which have strong resource-constraints.
Jointly with Mobile AI Workshop, we have a challenge track on Real-Time Video Super-Resolution on an actual mobile accelerator. This challenge shares the dataset and task with Video Super-Resolution Track 1. of NTIRE 2021, which are a set of paired high- and low-resolution training/testing video clips and the upscaling factor of x4. However, this challenge aims to obtain efficient solutions between high restoration accuracies (PSNR) and low resource consumptions (latency). Participants are encouraged to check out research areas of not only video super-resolution but neural architecture search, network quantization, and network pruning such as BasicVSR, RRN-L, RLSP, AdderNet, ProxylessNAS, FGNAS, DAQ, and TA+TSNet.
The top-ranked participants will be awarded and invited to follow the CVPR submission guide for workshops to describe their solution and to submit to the associated MAI workshop at CVPR 2021.
To learn more about the competition, to participate in the challenge, and to access the dataset with low and high resolution image pairs everybody is invited to register.
The training data is already made available to the registered participants.
In this challenge, each submission is validated based on the following two metrics:
1. The quality of the reconstructed results.
2. The runtime of the model on the actual target mobile platform.
The evaluation of the reconstructed results consists from the comparison of the reconstructed video frames to the original high-quality high-resolution video. The description of the runtime evaluation can be found in the corresponding tab.
For Fidelity, we use the standard Peak Signal To Noise Ratio (PSNR) and, complementary, the Structural Similarity (SSIM) index as often employed in the literature. Implementations are found in most of the image processing toolboxes. For each dataset, we report the average results over all the processed images belonging to it.
For submitting the results, you need to follow these steps:
Runtime per frame [ms] : 10.43
Mobile device: Samsung Galaxy S10 (Exynos)
Acceleration : TFLite GPU Delegate
Model size [Mb] : 3
Extra Data  / No Extra Data  : 1
Other description : Solution based on A+ of Timofte et al. ACCV 2014. The method was trained on Train 91 of Yang et al. and BSDS 200 of the Berkeley segmentation dataset.
During the final test phase, the participants will be asked to submit all the restored frames by email (in contrast to the CodaLab submission). The submitted result must be from the same method that generated the last submission to the CodaLab. We will check the consistency. Otherwise, the submission is void.
In this challenge, each submission is validated based on the following two metrics:
1. The quality of the reconstructed results.
2. The runtime of the model on the actual target mobile platform.
To run your model on a mobile device, it has first to be converted to TensorFlow Lite (TFLite) format. Please find the conversion instructions for TensorFlow, Keras and PyTorch models below:
It is fairly easy to get a TFLite model when using these two libraries. The following code will show you how to convert a simple U-Net model to the target TFLite format with TensorFlow and Keras libs:
PyTorch model can be generally converted to TFLite format using the following pipeline:
PyTorch -> ONNX -> TensorFlow -> TensorFlow Lite
However, we do not recommend using PyTorch for exporting your model to TFLite because of the following issues:
Despite the above mentioned issues, we will accept and evaluate your model if it was originally trained with PyTorch and you managed to convert it to TFLite. Additionally, you can find an instruction on how to convert a simple PyTorch U-Net model to TFLite below:
After you obtained a TFLite model, you can check its runtime using the following two options:
a. Run it remotely on the target evaluation platform (the instructions will be announced soon).
b. Run it on your own smartphone.
We strongly encourage you to do all main testing on your own device since:
1) If it is not working on your smartphone, then it will also not work on the target platform.
2) It is much more convenient and efficient to debug / profile the model locally.
3) You have an unlimited number of runs on your own device.
To run the resulting TFLite model on your smartphone, you can use AI Benchmark application allowing you to execute the model on mobile CPU, GPU, DSP and NPU:
1. Download AI Benchmark from the Google Play / website and run its standard tests.
2. After the end of the tests, enter the PRO Mode and select the Custom Model tab there.
3. Rename the exported TFLite model to model.tflite and put it into the Download folder of your device.
4. Select your mode type, the desired acceleration / inference options and run the model.
That's it - you should see the runtime, initialization time and RAM consumption of your model. Now you can try to play with different TF ops, layers and resolutions to improve all these parameters and get a very fast and efficient network!
Each time you try a new model architecture - try to initialize it with random weights, then convert it to TFLite and run on your own device using AI Benchmark. While this will take you only a few minutes, you will be sure that the model you are developing can be successfully converted and executed on a mobile device, and besides that you will get its estimated runtime compared to your previous entry.
The runtime of your final model will be evaluated on the OPPO Find X2 smartphone having a powerful Adreno 650 GPU capable of accelerating floating-point networks. The input tensor of your model should accept 10 subsequent video frames and have a size of [1 x 180 x 320 x 30], where the first dimension is the batch size, the second and third dimensions are the height and width of the input frames from the REDS dataset, and the last dimension is the number of channels (3 color channels x 10 frames). The size of the output tensor of your model should be [1 x 720 x 1280 x 30].
To check the validity of your model, you should run it on your own device using AI Benchmark (FP16 mode + TFLite GPU delegate) as the same setup will be used for the final runtime evaluation. The same also refers to model optimization: the faster is the speed of the network on your own smartphone - the faster will be its runtime on the OPPO device (as the same GPU acceleration backend will be used in both cases), which provides you with a very straightforward way how to test and optimize the runtime of your network.
These are the official rules (terms and conditions) that govern how the Mobile AI 2021 challenge on example-based video super-resolution will operate. This challenge will be simply reffered to as the "challenge" or the "contest" throghout the remaining part of these rules and may be named as "Mobile AI" or "REDS" benchmark, challenge, or contest, elsewhere (our webpage, our documentation, other publications).
In these rules, "we", "our", and "us" refer to the organizers ( Andres Romero (roandres [at] ethz.ch) Heewon Kim (ghimhw [at] gmail.com), Andrey Ignatov (andrey [at] vision.ee.ethz.ch), and Radu Timofte (Radu.Timofte [at] vision.ee.ethz.ch) ) of Mobile AI challenge and "you" and "yourself" refer to an eligible contest participant.
Note that these official rules can change during the contest until the start of the final phase. If at any point during the contest the registered participant considers that can not anymore meet the eligibility criteria or does not agree with the changes in the official terms and conditions then it is the responsability of the participant to send an email to the organizers such that to be removed from all the records. Once the contest is over no change is possible in the status of the registered participants and their entries.
This is a skill-based contest and chance plays no part in the determination of the winner(s).
The goal of the contest is to super-resolve an input video to an output video in the spatial domain with a upsampling factor x4 and the challenge is called video super-resolution.
Focus of the contest: It will be made available REDS dataset adapted for the specific needs of the challenge. The images have a large diversity of contents. We will refer to this dataset, its partition, and related materials as REDS. The dataset is divided into training, validation, and testing data. The aim is to achieve real-time (30 ms / frame) super-resolved output videos with the highest fidelity (PSNR) to the ground truth. The participants will not have access to the ground truth images from the test data. The ranking of the participants is according to the performance metric related with PSNR and latency of their methods on the test data. The participants will provide descriptions of their methods, details on energy consumption, GPU memory usage, model size, and (extra) data used for modeling. The winners will be determined according to their entries, the reproducibility of the results and uploaded codes or executables, and the above mentioned criteria as judged by the organizers.
The registered participants will be notified if any changes are made to the schedule. The schedule is available on the Mobile AI workshop web page and on the Overview of the Codalab competition.
You are eligible to register and compete in this contest only if you meet all the following requirements:
This contest is void wherever it is prohibited by law.
Entries submitted but not qualified to enter the contest, it is considered voluntary and for any entry you submit, Mobile AI reserves the right to evaluate it for scientific purposes, however, under no circumstances will such entries qualify for sponsored prizes. If you are an employee, affiliated with or representant of any of the Mobile AI challenge sponsors then you are allowed to enter in the contest and get ranked, however, if you will rank among the winners with eligible entries you will receive only a diploma award and none of the sponsored money, products or travel grants.
NOTE: Industry and research labs are allowed to submit entries and to compete in both the validation phase and final test phase. However, in order to get officially ranked on the final test leaderboard and to be eligible for awards the reproducibility of the results is a must and, therefore, the participants need to make available and submit their codes or executables. All the top entries will be checked for reproducibility and marked accordingly.
We will have 3 categories of entries in the final test ranking:
In order to be eligible for judging, an entry must meet all the following requirements:
Entry contents: The participants are required to submit image results and code or executables. To be eligible for prizes, the top ranking participants should publicly release their code or executables under a license of their choice, taken among popular OSI-approved licenses (http://opensource.org/licenses) and make their code or executables online accessible for a period of not less than one year following the end of the challenge (applies only for top three ranked participants of the competition). To enter the final ranking the participants will need to fill out a survey (fact sheet) briefly describing their method. All the participants are also invited (not mandatory) to submit a paper for peer-reviewing and publication at the Mobile AI Workshop and Challenges (to be held on November 2, 2019, Seoul, Korea). To be eligible for prizes, the participants' score must improve the baseline performance provided by the challenge organizers.
Use of the provided data: All data provided by Mobile AI are freely available to the participants from the website of the challenge under license terms provided with the data. The data are available only for open research and educational purposes, within the scope of the challenge. Mobile AI and the organizers make no warranties regarding the database, including but not limited to warranties of non-infringement or fitness for a particular purpose. The copyright of the images remains in the property of their respective owners. By downloading and making use of the data, you accept full responsibility for using the data. You shall defend and indemnify Mobile AI and the organizers, including their employees, Trustees, officers and agents, against any and all claims arising from your use of the data. You agree not to redistribute the data without this notice.
Test data: The organizers will use the test data for the final evaluation and ranking of the entries. The ground truth test data will no be made available to the participants during the contest.
Training and validation data: The organizers will make available to the participants a training and a validation dataset with ground truth images.
Post-challenge analyses: The organizers may also perform additional post-challenge analyses using extra-data, but without effect on the challenge ranking.
Submission: The entries will be online submitted via the CodaLab web platform. During the development phase, while the validation server is online, the participants will receive immediate feedback on validation data. The final evaluation will be computed automatically on the test data submissions, but the final scores will be released after the challenge is over.
Original work, permissions: In addition, by submitting your entry into this contest you confirm that, to the best of your knowledge: - your entry is your own original work; and - your entry only includes material that you own, or that you have permission to use.
Other than what is set forth below, we are not claiming any ownership rights to your entry. However, by submitting your entry, you:
Are granting us an irrevocable, worldwide right and license, in exchange for your opportunity to participate in the contest and potential prize awards, for the duration of the protection of the copyrights to:
Agree to sign any necessary documentation that may be required for us and our designees to make use of the rights you granted above;
Understand and acknowledge that us and other entrants may have developed or commissioned materials similar or identical to your submission and you waive any claims you may have resulting from any similarities to your entry;
Understand that we cannot control the incoming information you will disclose to our representatives or our co-sponsor’s representatives in the course of entering, or what our representatives will remember about your entry. You also understand that we will not restrict work assignments of representatives or our co-sponsor’s representatives who have had access to your entry. By entering this contest, you agree that use of information in our representatives’ or our co-sponsor’s representatives unaided memories in the development or deployment of our products or services does not create liability for us under this agreement or copyright or trade secret law;
Understand that you will not receive any compensation or credit for use of your entry, other than what is described in these official rules.
If you do not want to grant us these rights to your entry, please do not enter this contest.
The participants will follow the instructions on the CodaLab website to submit entries
The participants will be registered as mutually exclusive teams. Each CodaLab account is allowed to submit only one single final entry. Multiple submissions from a single team is valid only when they are significantly different. It is recommended to check the validity with the organizers in advance. We are not responsible for entries that we do not receive for any reason, or for entries that we receive but do not work properly.
The participants must follow the instructions and the rules. We will automatically disqualify incomplete or invalid entries.
The board of Mobile AI will select a panel of judges to judge the entries; all judges will be forbidden to enter the contest and will be experts in causality, statistics, machine learning, computer vision, or a related field, or experts in challenge organization. A list of the judges will be made available upon request. The judges will review all eligible entries received and select three winners for each of the two competition tracks based upon the prediction score on test data. The judges will verify that the winners complied with the rules, including that they documented their method by filling out a fact sheet.
The decisions of these judges are final and binding. The distribution of prizes according to the decisions made by the judges will be made within three (3) months after completion of the last round of the contest. If we do not receive a sufficient number of entries meeting the entry requirements, we may, at our discretion based on the above criteria, not award any or all of the contest prizes below. In the event of a tie between any eligible entries, the tie will be broken by giving preference to the earliest submission, using the time stamp of the submission platform.
The financial sponsors of this contest are listed on Mobile AI 2021 workshop web page. There will be economic incentive prizes and travel grants for the winners (based on availability) to boost contest participation; these prizes will not require participants to enter into an IP agreement with any of the sponsors, to disclose algorithms, or to deliver source code to them. The participants affiliated with the industry sponsors agree to not receive any sponsored money, product or travel grant in the case they will be among the winners.
Incentive Prizes for each track competitions (tentative, the prizes depend on attracted funds from the sponsors)
1st place: $?00 + ?award certificate
2nd place: $?00 + ?award certificate
3rd place: $?00 + ?award certificate
Publishing papers is optional and will not be a condition to entering the challenge or winning prizes. The top ranking participants are invited to submit a maximum 8-pages paper (CVPR 2021 author rules) for peer-reviewing to Mobile AI workshop.
The results of the challenge will be published together with Mobile AI 2021 workshop papers in the 2021 CVPR Workshops proceedings.
The top ranked participants and participants contributing interesting and novel methods to the challenge will be invited to be co-authors of the challenge report paper which will be published in the 2021 CVPR Workshops proceedings. A detailed description of the ranked solution as well as the reproducibility of the results are a must to be an eligible co-author.
If there is any change to data, schedule, instructions of participation, or these rules, the registered participants will be notified at the email they provided with the registration.
Within seven days following the determination of winners, we will send a notification to the potential winners. If the notification that we send is returned as undeliverable, or you are otherwise unreachable for any reason, we may award the prize to an alternate winner, unless forbidden by applicable law.
The prize such as money, product, or travel grant will be delivered to the registered team leader given that the team is not affiliated with any of the sponsors. It's up to the team to share the prize. If this person becomes unavailable for any reason, the prize will be delivered to be the authorized account holder of the e-mail address used to make the winning entry.
If you are a potential winner, we may require you to sign a declaration of eligibility, use, indemnity and liability/publicity release and applicable tax forms. If you are a potential winner and are a minor in your place of residence, and we require that your parent or legal guardian will be designated as the winner, and we may require that they sign a declaration of eligibility, use, indemnity and liability/publicity release on your behalf. If you, (or your parent/legal guardian if applicable), do not sign and return these required forms within the time period listed on the winner notification message, we may disqualify you (or the designated parent/legal guardian) and select an alternate selected winner.
Following the previous NTIRE and AIM challenges, we accept multiple submissions from a single team if they are significantly different. However, solutions with minor differences can be rejected by the organizers. For example, if solution A and B are submitted where B is an ensemble method of A, we do not consider them to be essentially different.
If you are preparing multiple submissions, please contact the organizers in advance for clarification.
The terms and conditions are inspired by and use verbatim text from the `Terms and conditions' of ChaLearn Looking at People Challenges and of the NTIRE 2017, 2018, 2019, 2020, and 2021 challenges and the AIM 2019, 2020 challenges.
The Mobile AI challenge on real image denoising is organized jointly with the Mobile AI 2021 workshop. The results of the challenge will be published at Mobile AI 2021 workshop and in the CVPR 2021 Workshops proceedings.
Andres Romero (roandres [at] ethz.ch), Heewon Kim (ghimhw [at] gmail.com), Andrey Ignatov (andrey [at] vision.ee.ethz.ch), and Radu Timofte (Radu.Timofte [at] vision.ee.ethz.ch) are the contact persons and direct managers of the Mobile AI challenge.
More information about Mobile AI workshop and challenge organizers is available here: http://ai-benchmark.com/workshops/mai/2021/
Start: Jan. 5, 2021, midnight
Description: Development phase - submit the results on the validation data.
Start: March 8, 2021, midnight
Description: Testing phase - submit the results on the test data.
March 8, 2021, 11:59 p.m.
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