$20000
MNIST Digit Recognition Challenge
Build a machine learning model to recognize single digits on an image.
Accenture
2 Participants
2 Submissions
Brief
MNIST ("Modified National Institute of Standards and Technology") is the de facto “hello world” dataset of computer vision. Since its release in 1999, this classic dataset of handwritten images has served as the basis for benchmarking classification algorithms. As new machine learning techniques emerge, MNIST remains a reliable resource for researchers and learners alike.
Prizes
1st Prize ($ 10000)
2nd Prize ($ 6000)
3rd Prize ($ 4000)
Timeline
- 27 May 2021 Competition Starts
- 30 Jun 2021 Competition Ends
- 23 Jul 2021 Winners Announced (Subject to change based on submission results)
Data Breakdown
The data files mnist_train.csv and mnist_test.csv contain gray-scale images of hand-drawn digits, from zero through nine.
Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255, inclusive.
The training data set, (mnist_train.csv), has 785 columns. The first column, called "label", is the digit that was drawn by the user. The rest of the columns contain the pixel-values of the associated image.
Each pixel column in the training set has a name like pixelx, where x is an integer between 0 and 783, inclusive. To locate this pixel on the image, suppose that we have decomposed x as x = i * 28 + j, where i and j are integers between 0 and 27, inclusive. Then pixelx is located on row i and column j of a 28 x 28 matrix, (indexing by zero).
The test data set, (test.csv), is the same as the training set, except that it does not contain the "label" column.
Your submission file should be in the following format: For each of the 28000 images in the test set, output a single line containing the ImageId and the digit you predict. For example, if you predict that the first image is of a 3, the second image is of a 7, and the third image is of a 8, then your submission file would look like:
ImageId,Label
1,3
2,7
3,8
(9997 more lines)
The evaluation metric for this contest is the categorization accuracy, or the proportion of test images that are correctly classified. For example, a categorization accuracy of 0.97 indicates that you have correctly classified all but 3% of the images.
FAQs
Who do I contact if I need help regarding a competition?
For any inquiries, please contact us at [email protected]
How will I know if I’ve won?
If you are one of the top three winners for this competition, we will email you with the final result and information about how to claim your reward.
How can I report a bug?
Please shoot us an email at [email protected] with details and a description of the bug you are facing, and if possible, please attach a screenshot of the bug itself.
If I win, how can I receive my reward?
Prizes will be paid by bank transfer. If for some reason you are not able to accept payment by bank transfer, please let us know and we will do our best to accommodate your needs as possible.
Rules
1. This competition is governed by the following Terms of Participation. Participants must agree to and comply with these Terms to participate.
2. Users can make a maximum number of 3 submissions per day. If users want to submit new files after making 5 submissions in a day, they will have to wait until the following day to do so. Please keep this in mind when uploading a submission.csv file. Any attempt to circumvent stated limits will result in disqualification.
3. The use of external datasets is not allowed.
4. It is not allowed to upload the competition dataset to other websites. Users who do not comply with this rule will be disqualified.
5. A competition prize will be awarded after we have received, successfully executed, and confirmed the validity of both the code and the solution. Once winners are announced and our team reaches out to them, the winners must provide the following by July 13, 2021 to be qualified as a competition winner and receive their prize:
a. All source files required to preprocess the data
b. All source files required to build, train and make predictions with the model using the processed data
c. A requirements.txt (or equivalent) file indicating all the required libraries and their versions as needed
d. A ReadMe file containing the following:
• Clear and unambiguous instructions on how to reproduce the predictions from start to finish including data pre-processing, feature extraction, model training and predictions generation
• Environment details regarding where the model was developed and trained, including OS, memory (RAM), disk space, CPU/GPU used, and any required environment configurations required to execute the code
• Clear answers to the following questions:
- Which data files are being used?
- How are these files processed?
- What is the algorithm used and what are its main hyperparameters?
- Any other comments considered relevant to understanding and using the model
In the event these items are not provided or do not meet the minimum requirements listed above, we will not be able to award the winner with their respective prize.
6. The submitted solution should be able to generate exactly the same output that gives the corresponding score on the leaderboard. If the score obtained from the code is different from what’s shown on the leaderboard, the new score will be used for the final rankings unless a logical explanation is provided.
7. Any prize awards are subject to verification of eligibility and compliance with these Terms of Participation. All decisions of bitgrit and the Competition Sponsor will be final and binding on all matters relating to this Competition.
8. Payments to winners may be subject to local, state, federal and foreign tax reporting and withholding requirements.
9. If two or more participants have the same score on the leaderboard, the participant who submitted the winning file first will be considered the winner.
10. All submissions need to be made as an individual; no teams are allowed in this competition. Users who do not comply with this rule will be immediately disqualified in the case that we find the same or very similar scores and/or uploaded solutions.
11. Any Participant shall delete the Company-Provided Information immediately after the completion of a Competition.
12. If you have any inquiries about this competition, please don’t hesitate to reach out to us at [email protected]. We ask that users do not contact Accenture directly.
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Non-Disclosure Agreement (NDA)
An agreement to not reveal the information shared regarding this competition to others.
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