{ "fetched_at": "2026-10-24T13:22:49Z", "source_url": "https://www.kaggle.com/competitions/google-universal-image-embedding/data", "pages": [ { "name": "rules", "content": "*Rules Updated August 17, 2022 - See Section A. COMPETITION-SPECIFIC RULES*\n\n### One account per participant\n\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n\n### No private sharing outside teams\n\nPrivately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all participants on the forums.\n\n### Team Mergers\n\nTeam mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the Team Merger Deadline. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.\n\n### Team Limits\n\nThe maximum team size is 5.\n\n### Submission Limits\n\nYou may submit a maximum of 5 entries per day.\n\nYou may select up to 2 final submissions for judging.\n\n### Competition Timeline\n\nCompetition Timeline dates (including Entry Deadline, Final Submission Deadline, Start Date, and Team Merger Deadline, as applicable) are reflected on the competition’s Overview > Timeline page.\n\n### COMPETITION-SPECIFIC TERMS\n\n**COMPETITION TITLE**: Google Universal Image Embedding\n\n**COMPETITION SPONSOR**: Google LLC\n\n**COMPETITION SPONSOR ADDRESS**: 1600 Amphitheatre Pkwy, Mountain View, CA 94043\n\n**COMPETITION WEBSITE**: https://www.kaggle.com/competitions/google-universal-image-embedding\n\n**TOTAL PRIZES AVAILABLE: $50,000**\n\nFirst Prize: $15,000\n\nSecond Prize: $10,000\n\nThird Prize: $8,000\n\nFourth Prize: $7,000\n\nFifth Prize: $5,000\n\nSixth Prize: $5,000\n\n**WINNER LICENSE TYPE**: OPEN SOURCE - Apache 2.0\n\nCompetitions are open to residents of the United States and worldwide, except that if you are a resident of Crimea, so-called Donetsk People's Republic (DNR) or Luhansk People's Republic (LNR), Cuba, Iran, Syria, North Korea, or are subject to U.S. export controls or sanctions, you may not enter the Competition. Other local rules and regulations may apply to you, so please check your local laws to ensure that you are eligible to participate in skills-based competitions. The Competition Sponsor reserves the right to award alternative Prizes where needed to comply with local laws.\n\n**ENTRY IN THIS COMPETITION CONSTITUTES YOUR ACCEPTANCE OF THESE OFFICIAL COMPETITION RULES.**\n\nThe Competition named above is a skills-based competition to promote and further the field of data science. You must register via the Competition Website to enter. Your competition submissions (\"Submissions\") must conform to the requirements stated on the Competition Website. Your Submissions will be scored based on the evaluation metric described on the Competition Website. Subject to compliance with the Competition Rules, Prizes, if any, will be awarded to participants with the best scores, based on the merits of the data science models submitted. See below for the complete Competition Rules.\n\n### A. COMPETITION-SPECIFIC RULES\n\nIn addition to the provisions of the General Competition Rules below, you understand and agree to these Competition-Specific Rules required by the Competition Sponsor:\n\n#### **1. WINNER LICENSE.** \n\nUnder Section 11 (Winners Obligations) of the General Rules below, you hereby grant and will grant Competition Sponsor the following license(s) with respect to your Submission if you are a Competition winner:\n \n*Open Source*: In order to receive a prize, you will license the source code used to generate your winning Submission under an Apache 2.0 License that in no event limits commercial use of such code containing or depending on such code. In order to encourage participation from industry researchers, participants who are not able to fulfill this license requirement may retain their position on the leaderboard, but will not receive a prize. The Competition Sponsor and Kaggle reserve the right to review and verify the entrant's compliance with these Rules.\n\n#### **2. EXTERNAL DATA.** \nThe following provision supersedes General Rules Section 7.C. below: You may use data other than the Competition Data (“External Data”) to develop and test your Submissions. However, you will ensure the External Data is publicly available and equally accessible to use by all participants of the Competition for purposes of the competition at no cost to the other participants. You will disclose usage of the External Data on the competition forum by the Team Merger Deadline. The ability to use External Data under this Section 7.C (External Data) does not limit your other obligations under these Competition Rules, including but not limited to Section 11 (Winners Obligations).\n\n#### **3. DATA ACCESS AND USE.** \nThe following provision supersedes General Rules Section 7.A. below: \"The annotations are licensed by Google under CC BY 4.0 license. The images listed in this dataset are publicly available on the web, and may have different licenses. Google does not own their copyright. Note: while we tried to identify images that are licensed under a Creative Commons Attribution license, we make no representations or warranties regarding the license status of each image and you should verify the license for each image yourself.\" \n\n#### **4. AUTOMATED MACHINE LEARNING TOOLS (AMLT).** \n\nIndividual participants and Teams may use automated machine learning tool(s) (“AMLT”) (e.g., Google AutoML, H2O Driverless AI, etc.) to create a Submission, provided that the participant or Team ensures that they have an appropriate license to the AMLT such that they are able to comply with the Competition Rules. \n \nThe following provision is added to the end of Section 11 of the General Competition Rules below: “Individual participants and Teams who create a Submission using an AMLT may win a Prize. However, for clarity, the potential winner’s Submission must still meet the requirements of these Rules, including but not limited to Section A.1 (Winners License), Section B.11 (Winners Obligations), and Section B.17 (Warranty, Indemnity, and Release).” \n\n### B. GENERAL COMPETITION RULES\n#### **1.\tBINDING AGREEMENT.** \n\nTo enter the Competition, you must agree to these Official Competition Rules, which incorporate by reference the provisions and content of the Competition Website and any Specific Competition Rules above (collectively, the \"Rules\"). Please read these Rules carefully before entry to ensure you understand and agree. You further agree that submission of an entry in the Competition constitutes agreement to these Rules. You may not submit an entry to the Competition and are not eligible to receive the prizes associated with this Competition (“Prizes”) unless you agree to these Rules. These Rules form a binding legal agreement between you and the Competition Sponsor with respect to the Competition.\n\n#### **2.\tELIGIBILITY.** \n\nA. To be eligible to enter the Competition, you must be:\n\n\t(i) a registered account holder at Kaggle.com; \n\n\t(ii) the older of 18 years old or the age of majority in your jurisdiction of residence (unless otherwise agreed to by Competition Sponsor and appropriate parental/guardian consents have been obtained by Competition Sponsor); \n\n\t(iii) not a resident of Crimea, so-called Donetsk People's Republic (DNR) or Luhansk People's Republic (LNR), Cuba, Iran, Syria, or North Korea; and\n\n\t(iv) not a person or representative of an entity under U.S. export controls or sanctions (see [https://www.treasury.gov/resource-center/sanctions/Programs/Pages/Programs.aspx][1]).\n\n\nIf you are entering as a representative of a company, educational institution or other legal entity, or on behalf of your employer, these rules are binding on you, individually, and the entity you represent or are an employee. If you are acting within the scope of your employment, as an employee, contractor, or agent of another party, you warrant that such party has full knowledge of your actions and has consented thereto, including your potential receipt of a Prize. You further warrant that your actions do not violate your employer's or entity's policies and procedures. \n\nThe Competition Sponsor reserves the right to verify eligibility and to adjudicate on any dispute at any time. If you provide any false information relating to the Competition concerning your identity, residency, mailing address, telephone number, email address, ownership of right, or information required for entering the Competition, you may be immediately disqualified from the Competition.\n\nB. Unless otherwise stated in the Specific Competition Rules above or prohibited by internal policies of the Competition Entities, employees, interns, contractors, officers and directors of Competition Entities may enter and participate in the Competition, but are not eligible to win any Prizes. \"Competition Entities\" means the Competition Sponsor, Kaggle Inc., and their respective parent companies, subsidiaries and affiliates. If you are such a participant from a Competition Entity, you are subject to all applicable internal policies of your employer with respect to your participation.\n\n#### **3.\tSPONSOR AND HOSTING PLATFORM.** \n\nThe Competition is sponsored by Competition Sponsor named above. The Competition is hosted on behalf of Competition Sponsor by Kaggle Inc. (\"Kaggle\"). Kaggle is an independent contractor of Competition Sponsor, and is not a party to this or any agreement between you and Competition Sponsor. You understand that Kaggle has no responsibility with respect to selecting the potential Competition winner(s) or awarding any Prizes. Kaggle will perform certain administrative functions relating to hosting the Competition, and you agree to abide by the provisions relating to Kaggle under these Rules. As a Kaggle.com account holder and user of the Kaggle competition platform, remember you have accepted and are subject to the Kaggle Terms of Service at [www.kaggle.com/terms][2] in addition to these Rules.\n\n#### **4.\tCOMPETITION PERIOD.** \n\nFor the purposes of Prizes, the Competition will run from the Start Date and time to the Final Submission Deadline (such duration the “Competition Period”). The Competition Timeline is subject to change, and Competition Sponsor may introduce additional hurdle deadlines during the Competition Period. Any updated or additional deadlines will be publicized on the Competition Website. It is your responsibility to check the Competition Website regularly to stay informed of any deadline changes. *YOU ARE RESPONSIBLE FOR DETERMINING THE CORRESPONDING TIME ZONE IN YOUR LOCATION.* \n\n#### **5.\tCOMPETITION ENTRY.** \n\nNO PURCHASE NECESSARY TO ENTER OR WIN. To enter the Competition, you must register on the Competition Website prior to the Entry Deadline, and follow the instructions for developing and entering your Submission through the Competition Website. Your Submissions must be made in the manner and format, and in compliance with all other requirements, stated on the Competition Website (the \"Requirements\"). Submissions must be received before any Submission deadlines stated on the Competition Website. Submissions not received by the stated deadlines will not be eligible to receive a Prize.\n\nSubmissions may not use or incorporate information from hand labeling or human prediction of the validation dataset or test data records.\n\nIf the Competition is a multi-stage competition with temporally separate training and/or test data, one or more valid Submissions may be required during each Competition stage in the manner described on the Competition Website in order for the Submissions to be Prize eligible.\n\nSubmissions are void if they are in whole or part illegible, incomplete, damaged, altered, counterfeit, obtained through fraud, or late. Competition Sponsor reserves the right to disqualify any entrant who does not follow these Rules, including making a Submission that does not meet the Requirements. \n\n#### **6.\tINDIVIDUALS AND TEAMS.** \n\nA. *Individual Account*. You may make Submissions only under one, unique Kaggle.com account. You will be disqualified if you make Submissions through more than one Kaggle account, or attempt to falsify an account to act as your proxy. You may submit up to the maximum number of Submissions per day as specified on the Competition Website. \n\nB. *Teams*. If permitted under the Competition Website guidelines, multiple individuals may collaborate as a team (a \"Team\"); however, you may join or form only one Team. Each Team member must be a single individual with a separate Kaggle account. You must register individually for the Competition before joining a Team. You must confirm your Team membership to make it official by responding to the Team notification message sent to your Kaggle account. Team membership may not exceed the Maximum Team Size stated on the Competition Website.\n\nC. *Team Merger*. Teams may request to merge via the Competition Website. Team mergers may be allowed provided that: (i) the combined Team does not exceed the Maximum Team Size; (ii) the number of Submissions made by the merging Teams does not exceed the number of Submissions permissible for one Team at the date of the merger request; (iii) the merger is completed before the earlier of: any merger deadline or the Competition deadline; and (iv) the proposed combined Team otherwise meets all the requirements of these Rules. \n\n#### **7.\tCOMPETITION DATA.** \n\n\"Competition Data\" means the data or datasets available from the Competition Website for the purpose of use in the Competition, including any prototype or executable code provided on the Competition Website. The Competition Data will contain private and public test sets. Which data belongs to which set will not be made available to participants. \n\nA. *Data Access and Use*. \n*Competition Use and Non-Commercial & Academic Research is checked*: You may access and use the Competition Data for non-commercial purposes only, including for participating in the Competition and on Kaggle.com forums, and for academic research and education. The Competition Sponsor reserves the right to disqualify any participant who uses the Competition Data other than as permitted by the Competition Website and these Rules.\n\n\nB. *Data Security*. You agree to use reasonable and suitable measures to prevent persons who have not formally agreed to these Rules from gaining access to the Competition Data. You agree not to transmit, duplicate, publish, redistribute or otherwise provide or make available the Competition Data to any party not participating in the Competition. You agree to notify Kaggle immediately upon learning of any possible unauthorized transmission of or unauthorized access to the Competition Data and agree to work with Kaggle to rectify any unauthorized transmission or access.\n\nC. *External Data*. You may use data other than the Competition Data (“External Data”) to develop and test your Submissions. However, you will ensure the External Data is publicly available and equally accessible to use by all participants of the Competition for purposes of the competition at no cost to the other participants. The ability to use External Data under this Section 7.C (External Data) does not limit your other obligations under these Competition Rules, including but not limited to Section 11 (Winners Obligations). \n\n#### **8.\tSUBMISSION CODE REQUIREMENTS.**\n\nA. *Private Code Sharing*. Unless otherwise specifically permitted under the Competition Website or Competition Specific Rules above, during the Competition Period, you are not allowed to privately share source or executable code developed in connection with or based upon the Competition Data or other source or executable code relevant to the Competition (“Competition Code”). This prohibition includes sharing Competition Code between separate Teams, unless a Team merger occurs. Any such sharing of Competition Code is a breach of these Competition Rules and may result in disqualification.\n\nB. *Public Code Sharing*. You are permitted to publicly share Competition Code, provided that such public sharing does not violate the intellectual property rights of any third party. If you do choose to share Competition Code or other such code, you are required to share it on Kaggle.com on the discussion forum or notebooks associated specifically with the Competition for the benefit of all competitors. By so sharing, you are deemed to have licensed the shared code under an Open Source Initiative-approved license (see [www.opensource.org][3]) that in no event limits commercial use of such Competition Code or model containing or depending on such Competition Code.\n\nC. *Use of Open Source*. Unless otherwise stated in the Specific Competition Rules above, if open source code is used in the model to generate the Submission, then you must only use open source code licensed under an Open Source Initiative-approved license (see [www.opensource.org][4]) that in no event limits commercial use of such code or model containing or depending on such code.\n\n#### **9.\tDETERMINING WINNERS.** \n\nEach Submission will be scored and ranked by the evaluation metric stated on the Competition Website. During the Competition Period, the current ranking will be visible on the Competition Website's public leaderboard. The potential winner(s) are determined solely by the leaderboard ranking on the private leaderboard, subject to compliance with these Rules. The public leaderboard will be based on the public test set and the private leaderboard will be based on the private test set.\n\nIn the event of a tie, the Submission that was entered first to the Competition will be the winner. In the event a potential winner is disqualified for any reason, the Submission that received the next highest score rank will be chosen as the potential winner.\n\n#### **10.\tNOTIFICATION OF WINNERS & DISQUALIFICATION.**\n\nThe potential winner(s) will be notified by email. \n \nIf a potential winner (i) does not respond to the notification attempt within one (1) week from the first notification attempt or (ii) notifies Kaggle within one week after the Final Submission Deadline that the potential winner does not want to be nominated as a winner or does not want to receive a Prize, then, in each case (i) and (ii) such potential winner will not receive any Prize, and an alternate potential winner will be selected from among all eligible entries received based on the Competition’s judging criteria.\n \nIn case (i) and (ii) above Kaggle may disqualify the participant. However, in case (ii) above, if requested by Kaggle, such potential winner may provide code and documentation to verify the participant’s compliance with these Rules. If the potential winner provides code and documentation to the satisfaction of Kaggle, the participant will not be disqualified pursuant to this paragraph.\n \nCompetition Sponsor reserves the right to disqualify any participant from the Competition if the Competition Sponsor reasonably believes that the participant has attempted to undermine the legitimate operation of the Competition by cheating, deception, or other unfair playing practices or abuses, threatens or harasses any other participants, Competition Sponsor or Kaggle.\n \nA disqualified participant may be removed from the Competition leaderboard, at Kaggle's sole discretion. If a Participant is removed from the Competition Leaderboard, additional winning features associated with the Kaggle competition platform, for example Kaggle points or medals, may also not be awarded.\n \nThe final leaderboard list will be publicly displayed at Kaggle.com. Determinations of Competition Sponsor are final and binding.\n\n#### **11.\tWINNERS OBLIGATIONS.**\n\nAs a condition to being awarded a Prize, a Prize winner must fulfill the following obligations:\n\n(a) deliver to the Competition Sponsor the final model's software code as used to generate the winning Submission and associated documentation. The delivered software code should follow [these documentation guidelines][5], must be capable of generating the winning Submission, and contain a description of resources required to build and/or run the executable code successfully. To the extent that the final model’s software code includes generally commercially available software that is not owned by you, but that can be procured by the Competition Sponsor without undue expense, then instead of delivering the code for that software to the Competition Sponsor, you must identify that software, method for procuring it, and any parameters or other information necessary to replicate the winning Submission;\n\n(b) grant to the Competition Sponsor the license to the winning Submission stated in the Competition Specific Rules above, and represent that you have the unrestricted right to grant that license; \n\n(c) sign and return all Prize acceptance documents as may be required by Competition Sponsor or Kaggle, including without limitation: (i) eligibility certifications; (ii) licenses, releases and other agreements required under the Rules; and (iii) U.S. tax forms (such as IRS Form W-9 if U.S. resident, IRS Form W-8BEN if foreign resident, or future equivalents).\n\n#### **12.\tPRIZES.** \n\nPrize(s) are as described on the Competition Website and are only available for winning during the time period described on the Competition Website. The odds of winning any Prize depends on the number of eligible Submissions received during the Competition Period and the skill of the participants. \n\nAll Prizes are subject to Competition Sponsor's review and verification of the participant’s eligibility and compliance with these Rules, and the compliance of the winning Submissions with the Submissions Requirements. In the event that the Submission demonstrates non-compliance with these Competition Rules, Competition Sponsor may at its discretion take either of the following actions: (i) disqualify the Submission(s); or (ii) require the potential winner to remediate within one week after notice all issues identified in the Submission(s) (including, without limitation, the resolution of license conflicts, the fulfillment of all obligations required by software licenses, and the removal of any software that violates the software restrictions).\n\nA potential winner may decline to be nominated as a Competition winner in accordance with Section 10.\n\nPotential winners must return all required Prize acceptance documents within two (2) weeks following notification of such required documents, or such potential winner will be deemed to have forfeited the prize and another potential winner will be selected. Prize(s) will be awarded within approximately 30 days after receipt by Competition Sponsor or Kaggle of the required Prize acceptance documents. Transfer or assignment of a Prize is not allowed. \n\nYou are not eligible to receive any Prize if you do not meet the Eligibility requirements in Section 2 above.\n\nIf a Team wins a monetary Prize, the Prize money will be allocated in even shares between the eligible Team members, unless the Team unanimously opts for a different Prize split and notifies Kaggle before Prizes are issued.\n\n#### **13.\tTAXES.** \n\nALL TAXES IMPOSED ON PRIZES ARE THE SOLE RESPONSIBILITY OF THE WINNERS. Payments to potential winners are subject to the express requirement that they submit all documentation requested by Competition Sponsor or Kaggle for compliance with applicable state, federal, local and foreign (including provincial) tax reporting and withholding requirements. Prizes will be net of any taxes that Competition Sponsor is required by law to withhold. If a potential winner fails to provide any required documentation or comply with applicable laws, the Prize may be forfeited and Competition Sponsor may select an alternative potential winner. Any winners who are U.S. residents will receive an IRS Form-1099 in the amount of their Prize.\n\n#### **14.\tGENERAL CONDITIONS.**  \n\nAll federal, state, provincial and local laws and regulations apply. \n\n#### **15.\tPUBLICITY.**\n\nYou agree that Competition Sponsor, Kaggle and its affiliates may use your name and likeness for advertising and promotional purposes without additional compensation, unless prohibited by law.\n\n#### **16.\tPRIVACY.**\n\nYou acknowledge and agree that Competition Sponsor and Kaggle may collect, store, share and otherwise use personally identifiable information provided by you during the Kaggle account registration process and the Competition, including but not limited to, name, mailing address, phone number, and email address (“Personal Information”). Kaggle acts as an independent controller with regard to its collection, storage, sharing, and other use of this Personal Information, and will use this Personal Information in accordance with its Privacy Policy <[www.kaggle.com/privacy][6]>, including for administering the Competition. As a Kaggle.com account holder, you have the right to request access to, review, rectification, portability or deletion of any personal data held by Kaggle about you by logging into your account and/or contacting Kaggle Support at <[www.kaggle.com/contact][7]>.\n\n As part of Competition Sponsor performing this contract between you and the Competition Sponsor, Kaggle will transfer your Personal Information to Competition Sponsor, which acts as an independent controller with regard to this Personal Information. As a controller of such Personal Information, Competition Sponsor agrees to comply with all U.S. and foreign data protection obligations with regard to your Personal Information. Kaggle will transfer your Personal Information to Competition Sponsor in the country specified in the Competition Sponsor Address listed above, which may be a country outside the country of your residence. Such country may not have privacy laws and regulations similar to those of the country of your residence.\n\n#### **17.\tWARRANTY, INDEMNITY AND RELEASE.** \n\nYou warrant that your Submission is your own original work and, as such, you are the sole and exclusive owner and rights holder of the Submission, and you have the right to make the Submission and grant all required licenses. You agree not to make any Submission that: (i) infringes any third party proprietary rights, intellectual property rights, industrial property rights, personal or moral rights or any other rights, including without limitation, copyright, trademark, patent, trade secret, privacy, publicity or confidentiality obligations, or defames any person; or (ii) otherwise violates any applicable U.S. or foreign state or federal law.\n\nTo the maximum extent permitted by law, you indemnify and agree to keep indemnified Competition Entities at all times from and against any liability, claims, demands, losses, damages, costs and expenses resulting from any of your acts, defaults or omissions and/or a breach of any warranty set forth herein. To the maximum extent permitted by law, you agree to defend, indemnify and hold harmless the Competition Entities from and against any and all claims, actions, suits or proceedings, as well as any and all losses, liabilities, damages, costs and expenses (including reasonable attorneys fees) arising out of or accruing from: (a) your Submission or other material uploaded or otherwise provided by you that infringes any third party proprietary rights, intellectual property rights, industrial property rights, personal or moral rights or any other rights, including without limitation, copyright, trademark, patent, trade secret, privacy, publicity or confidentiality obligations, or defames any person; (b) any misrepresentation made by you in connection with the Competition; (c) any non-compliance by you with these Rules or any applicable U.S. or foreign state or federal law; (d) claims brought by persons or entities other than the parties to these Rules arising from or related to your involvement with the Competition; and (e) your acceptance, possession, misuse or use of any Prize, or your participation in the Competition and any Competition-related activity.\n\nYou hereby release Competition Entities from any liability associated with: (a) any malfunction or other problem with the Competition Website; (b) any error in the collection, processing, or retention of any Submission; or (c) any typographical or other error in the printing, offering or announcement of any Prize or winners.\n\n#### **18.\tINTERNET.** \n\nCompetition Entities are not responsible for any malfunction of the Competition Website or any late, lost, damaged, misdirected, incomplete, illegible, undeliverable, or destroyed Submissions or entry materials due to system errors, failed, incomplete or garbled computer or other telecommunication transmission malfunctions, hardware or software failures of any kind, lost or unavailable network connections, typographical or system/human errors and failures, technical malfunction(s) of any telephone network or lines, cable connections, satellite transmissions, servers or providers, or computer equipment, traffic congestion on the Internet or at the Competition Website, or any combination thereof, which may limit a participant’s ability to participate.\n\n#### **19.\tRIGHT TO CANCEL, MODIFY OR DISQUALIFY.**\n\nIf for any reason the Competition is not capable of running as planned, including infection by computer virus, bugs, tampering, unauthorized intervention, fraud, technical failures, or any other causes which corrupt or affect the administration, security, fairness, integrity, or proper conduct of the Competition, Competition Sponsor reserves the right to cancel, terminate, modify or suspend the Competition. Competition Sponsor further reserves the right to disqualify any participant who tampers with the submission process or any other part of the Competition or Competition Website. Any attempt by a participant to deliberately damage any website, including the Competition Website, or undermine the legitimate operation of the Competition is a violation of criminal and civil laws. Should such an attempt be made, Competition Sponsor and Kaggle each reserves the right to seek damages from any such participant to the fullest extent of the applicable law.\n\n#### **20.\tNOT AN OFFER OR CONTRACT OF EMPLOYMENT.**\n\nUnder no circumstances will the entry of a Submission, the awarding of a Prize, or anything in these Rules be construed as an offer or contract of employment with Competition Sponsor or any of the Competition Entities. You acknowledge that you have submitted your Submission voluntarily and not in confidence or in trust. You acknowledge that no confidential, fiduciary, agency, employment or other similar relationship is created between you and Competition Sponsor or any of the Competition Entities by your acceptance of these Rules or your entry of your Submission.\n\n#### **21.\tGOVERNING LAW.** \n\nUnless otherwise provided in the Competition Specific Rules above, all claims arising out of or relating to these Rules will be governed by California law, excluding its conflict of laws rules, and will be litigated exclusively in the Federal or State courts of Santa Clara County, California, USA. The parties consent to personal jurisdiction in those courts. If any provision of these Rules is held to be invalid or unenforceable, all remaining provisions of the Rules will remain in full force and effect.\n\n\n [1]: https://www.treasury.gov/resource-center/sanctions/Programs/Pages/Programs.aspx\n [2]: http://www.kaggle.com/terms\n [3]: http://www.opensource.org\n [4]: http://www.opensource.org\n [5]: https://www.kaggle.com/WinningModelDocumentationGuidelines\n [6]: http://www.kaggle.com/privacy\n [7]: http://www.kaggle.com/contact", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "Welcome to the Universal Image Embedding competition! After hosting challenges in the domain of landmarks for the past four years, this year we introduce the first competition in image representations that should work across many object types.\n\nImage representations are a critical building block of computer vision applications. Traditionally, research on image embedding learning has been conducted with a focus on per-domain models. Generally, papers propose generic embedding learning techniques which are applied to different domains separately, rather than developing generic embedding models which could be applied to all domains combined.\n\nIn this competition, the developed models are expected to retrieve relevant database images to a given query image (ie, the model should retrieve database images containing the same object as the query). The images in our dataset comprise a variety of object types, such as apparel, artwork, landmarks, furniture, packaged goods, among others.\n\nThis year's competition is structured in a representation learning format: you will create a model that extracts a feature embedding for the images and submit the model via Kaggle Notebooks. Kaggle will run your model on a held-out test set, perform a k-nearest-neighbors lookup, and score the resulting embedding quality. Both Tensorflow and PyTorch models are supported.\n\n> **This is a Code Competition. Refer to [Code Requirements](https://www.kaggle.com/c/google-universal-image-embedding/overview/code-requirements) for details.**\n\nCover image credits: [Chris Schrier, CC-BY](https://www.flickr.com/photos/schrierc/5502246181); [Petri Krohn, GNU Free Documentation License](https://commons.wikimedia.org/wiki/File:MOMA_chairs_2.jpg); [Drazen Nesic, CC0](https://pixnio.com/media/cartoon-textile-texture-funny-giraffe); [Marco Verch Professional Photographer, CCBY](https://www.flickr.com/photos/30478819@N08/44289962475); [Grendelkhan, CCBY](https://commons.wikimedia.org/wiki/File:Waymo_self-driving_car_side_view.gk.jpg); [Bobby Mikul, CC0](https://www.publicdomainpictures.net/en/view-image.php?image=16166&picture=empire-state-building); [Vincent Van Gogh, CC0](https://www.rawpixel.com/image/537438/the-starry-night-van-gogh); [pxhere.com, CC0](https://pxhere.com/en/photo/479766); [Smart Home Perfected, CC-BY](https://www.flickr.com/photos/smarthomeperfected/51048330253). ", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "## Metric\n\nSubmissions are evaluated according to the mean Precision @ 5 metric, where we introduce a small modification to avoid penalizing queries with fewer than 5 expected index images. In detail, the metric is computed as follows:\n\n$$mP@5 = \\frac{1}{Q} \\sum_{q=1}^{Q} \\frac{1}{min(n\\_q, 5)} \\sum\\_{j=1}^{min(n\\_q, 5)} rel\\_q(j)$$\n\nwhere:\n\n- \\\\(Q\\\\) is the number of query images\n- \\\\(n\\_q\\\\) is the number of index images containing an object in common with the query image \\\\(q\\\\). Note that \\\\(n_q \\gt 0\\\\).\n- \\\\(rel_q(j)\\\\) denotes the relevance of prediciton \\\\(j\\\\) for the \\\\(q\\\\)-th query: it’s 1 if the \\\\(j\\\\)-th prediction is correct, and 0 otherwise\n\n## Submission File\n\nUnlike a traditional Kaggle code competition where notebooks are rerun top-to-bottom on a private test set, in this competition you will be submitting a model file. The model must take an image as an input, and return a float vector (i.e., the image embedding) at the output. The embedding dimensionality should be no greater than **64**. Your model must be packaged into a `submission.zip` file, and compatible with either TensorFlow 2.6.4 or Pytorch 1.11.0. In most cases, scoring is expected to take a few hours to complete.\n\nThe `submission.zip` should contain one (and only one) of the following:\n\n- files and directories in the Tensorflow's [SavedModel format](https://www.tensorflow.org/tutorials/keras/save_and_load#savedmodel_format).\n- file in PyTorch’s [TorchScript format](https://pytorch.org/docs/stable/jit.html), named “saved_model.pt”.\n\nTo emphasize once again: the embedding produced by the model must be at most 64D (if not, the submission will error out).\n\n## Submission process\n\nKaggle will use the submitted model to:\n\n 1. Extract embeddings for the private test and index image sets.\n 2. Create a kNN (k = 5) lookup for each test sample, using the Euclidean distance between test and index embeddings.\n 3. Score the quality of the lookups using the competition metric.\n\n## Baseline kernels\n\nTo submit your model, all you need is a kernel that produces a `submission.zip` file containing the model. Here are examples:\n\n**Tensorflow.** See [this kernel](https://www.kaggle.com/code/francischen1991/tf-baseline-v2-submission) for the minimal baseline model and [this kernel](https://www.kaggle.com/andrefaraujo/tf-baseline-submission) for the more complicated baseline model.\n\n**PyTorch.** See [this kernel](https://www.kaggle.com/andrefaraujo/pytorch-baseline-submission).\n\n## Code example to produce model\n\nFor **Tensorflow**, we provide a minimal **Tensorflow** baseline model based on pretrained Inception-v3 model with a linear layer to project the output feature to 64D. In the end, the model is saved in the SavedModel format: \n\n```\nimport tensorflow as tf\n\nimage = tf.keras.layers.Input([None, None, 3], dtype=tf.uint8)\noutput = tf.cast(image, tf.float32)\noutput = tf.keras.layers.Lambda(lambda x: tf.image.resize(x, [224, 224]), name='resize')(output)\noutput = tf.keras.applications.inception_v3.preprocess_input(output)\noutput = tf.keras.applications.inception_v3.InceptionV3(weights='imagenet', input_shape=[None, None, 3],\n include_top=False, pooling='avg')(output)\noutput = tf.keras.layers.Dense(64, name='embedding')(output)\noutput = tf.keras.layers.Lambda(lambda x: tf.math.l2_normalize(x, axis=-1), name='embedding_norm')(output)\nmodel = tf.keras.Model(inputs=[image], outputs=[output])\nmodel.summary()\n\ntf.saved_model.save(model, 'inceptionv3/')\n```\nFor more complicated examples, please check the example given in [this repository](https://github.com/google-research/google-research/tree/master/universal_embedding_challenge). Specifically, [this script](https://github.com/google-research/google-research/blob/master/universal_embedding_challenge/export_saved_model.py) shows an example of model exporting, for a model that is trained with [this script](https://github.com/google-research/google-research/blob/master/universal_embedding_challenge/train.py).\n\nFor **PyTorch**, see a similar example below that uses pretrained Inception-v3 model and a linear projection layer, and exports the model in the TorchScript format:\n\n```\nimport torch\nimport torch.nn as nn\nfrom torchvision import models\nfrom torchvision import transforms\n\nclass MyModel(nn.Module):\n def __init__(self):\n super().__init__()\n inception_model = models.inception_v3(pretrained=True)\n inception_model.fc = nn.Linear(2048, 64)\n self.feature_extractor = inception_model\n \n def forward(self, x):\n x = transforms.functional.resize(x,size=[224, 224])\n x = x/255.0\n x = transforms.functional.normalize(x, \n mean=[0.485, 0.456, 0.406], \n std=[0.229, 0.224, 0.225])\n return self.feature_extractor(x).logits\n \nmodel = MyModel()\nmodel.eval()\nsaved_model = torch.jit.script(model)\nsaved_model.save('saved_model.pt')\n```\n\n## Feature extraction code\n\nWhen your model is submitted, we run it in the competition images in order to extract embeddings. Please find below code snippets that show how the model is used in detail. These could be helpful for participants to make sure that their models are extracting embeddings correctly.\n\n**Tensorflow**:\n\n```\nimport numpy as np\nfrom PIL import Image\nimport tensorflow as tf\n\n# Model loading.\nmodel = tf.saved_model.load(saved_model_path)\nembedding_fn = model.signatures[\"serving_default\"]\n\n# Load image and extract its embedding.\nimage_tensor = tf.convert_to_tensor(\n np.array(Image.open(image_path).convert(\"RGB\"))\n )\nexpanded_tensor = tf.expand_dims(image_tensor, axis=0)\nembedding = embedding_fn(expanded_tensor)[\"embedding_norm\"]\n```\n\n**PyTorch**:\n\n```\nfrom PIL import Image\nimport torch\nfrom torchvision import transforms\n\n# Model loading.\nmodel = torch.jit.load(saved_model_path)\nmodel.eval()\nembedding_fn = model\n\n# Load image and extract its embedding.\ninput_image = Image.open(image_path).convert(\"RGB\")\nconvert_to_tensor = transforms.Compose([transforms.PILToTensor()])\ninput_tensor = convert_to_tensor(input_image)\ninput_batch = input_tensor.unsqueeze(0)\nwith torch.no_grad():\n embedding = torch.flatten(embedding_fn(input_batch)[0]).cpu().data.numpy()\n```\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Timeline", "content": "* **July 11, 2022** - Start Date.\n\n* **October 3, 2022** - Entry Deadline. You must accept the competition rules before this date in order to compete.\n\n* **October 3, 2022** - Team Merger Deadline. This is the last day participants may join or merge teams.\n\n* **October 10, 2022** - Final Submission Deadline.\n\n* **October 17, 2022** - Paper Submission deadline (winners only).**\n\n**Winners will be required to provide a detailed description of their method in order to claim their prize (minimum of 2 pages double-column).**\n\nAll deadlines are at 11:59 PM UTC on the corresponding day unless otherwise noted. The competition organizers reserve the right to update the contest timeline if they deem it necessary.\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "data-description", "content": "In this competition, you are asked to develop models that can efficiently retrieve images from a large database. For each query image, the model is expected to retrieve the most similar images from an index set.\n\nWe do not provide a training set. In accordance with the [rules](https://www.kaggle.com/competitions/google-universal-image-embedding/rules), any training data may be used, as long as it is disclosed in the forum by the relevant deadline. There exist many public datasets for different object types (e.g., artworks, landmarks, products, etc), and we encourage participants to experiment with them as needed.\n\n\n\nOur evaluation dataset, which is kept private, contains images of the following types of object: apparel & accessories, packaged goods, landmarks, furniture & home decor, storefronts, dishes, artwork, toys, memes, illustrations and cars. The header image above shows examples of images in these domains, which are similar to the ones in the evaluation set. Labels are defined generally at the instance level, for example: the same T-shirt, the same building, the same painting, the same dish.\n\nThe following plot shows the distribution of object types in the dataset:\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3258%2Fa02c90ad69a049b11a39bd38abcde2cb%2Fdomains.png?generation=1657141626387503&alt=media)\n\n\n\nPlease see the [Evaluation](https://www.kaggle.com/competitions/google-universal-image-embedding/overview/evaluation) page for details on how the model should be formatted and submitted.\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Prizes", "content": "* 1st Place - $ 15,000\n* 2nd Place - $ 10,000\n* 3rd Place - $ 8,000\n* 4th Place - $ 7,000\n* 5th Place - $ 5,000\n* 6th Place - $ 5,000\n\n**Note**: The winners will additionally be required to provide a detailed description of their method in order to claim the prize (minimum of 2 pages double-column) by October 17, 2022, which is one week after the competition has concluded.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Code Requirements", "content": "\n\n### This is a Code Competition\n\nSubmissions to this competition must be made through Notebooks. In order for the \"Submit\" button to be active after a commit, the following conditions must be met:\n\n- CPU Notebook <= 9 hours run-time\n- GPU Notebook <= 9 hours run-time\n- Freely & publicly available external data is allowed, including pre-trained models\n- Submission file must contain a model and be named `submission.zip`\n\nPlease see the [Code Competition FAQ](https://www.kaggle.com/docs/competitions#notebooks-only-FAQ) for more information on how to submit. And review the [code debugging doc](https://www.kaggle.com/code-competition-debugging) if you are encountering submission errors.\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "ECCV 2022", "content": "This competition is part of the [Instance-Level Recognition workshop](https://ilr-workshop.github.io/ECCVW2022) at [ECCV 2022](https://eccv2022.ecva.net/). Top submissions for the competition will be invited to give talks at the workshop. Attending the workshop is not required to participate in the competition, however only teams that are attending the workshop will be considered to present their work.\n\nAttendees presenting in person are responsible for all costs associated with expenses and fees to attend [ECCV’2022](https://eccv2022.ecva.net/).\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }