Submission File:

For each image in the test dataset, your algorithm needs to predict a list of labels, and the corresponding bounding boxes.

The output is expected to contain the following two columns:
  • ImageId: the id of the test image, for example, Adachi_test_00000001
  • PredictionString: the prediction string should be a space-delimited of 5 integers. For example, 2 240 170 260 240 means it's label 2, with a bounding box of coordinates (x_min, y_min, x_max, y_max). We accept up to 5 predictions. For example, if you submit 3 42 24 170 186 1 292 28 430 198 4 168 24 292 190 5 299 238 443 374 2 160 195 294 357 6 3 214 135 356 which contains 6 bounding boxes, we will only take the first 5 into consideration.
Paper submission
  • After the competition phase is completed, a link for the submission of the accompanying academic paper will be provided to the top 10 participants as ranked by the public/private leaderboard weighting described above.
  • Peer reviewers will review the academic papers.
  • The papers are expected to conform to the IEEE 2-column format set by the conference, which can be found at Big Data CFP.
  • If you have questions, please feel free to contact the lead organizer of the dataset competition.
Contents in technical paper (Required):
  • Explanation of your method
  • Evaluation of your method (you can use results obtained on the site of road damage detection challenge)
  • Code and trained model link
Contents in technical paper (Optional but preferable):
  • Detailed evaluation of your results (inference speed, model size etc.)
  • Error Analysis: Examples of failed attempts, efforts that did not go well.
Your Code

Source code will also be required to be submitted, either through a publicly available repository on a Git-based version control hosting service such as GitHub or BitBucket for the final evaluation. All source codes are expected to be released as open-source software, utilizing some generally accepted licensing such as Apache License 2.0, GNU General Public License, MIT license, or others of similar acceptance by the Open Source Initiative.