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  "fetched_at": "2026-10-19T19:15:45Z",
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      "content": "The training and test images for the competition were all collected in the Monterey Bay Area between the surface and 1300 meters depth. The images contain bounding box annotations of 290 categories of bottom dwelling animals. The training and evaluation data are split across an 800 meter depth threshold: all training data is collected from 0-800 meters, evaluation data comes from the whole 0-1300 meter range. Since an organisms' habitat range is partially a function of depth, the species distributions in the two regions are overlapping but not identical. Test images are drawn from the same region but may come from above or below the depth horizon. \n\nThe competition goal is to label the animals present in a given image (i.e. multi-label classification) and determine whether the image is out-of-sample.    \n\n## Data format\nThe training dataset are provide in two different formats: multi-label classification and object detection. The different formats live in the corresponding named directories. The datasets in these directories are identical aside from how they are organized. \n\n### Multi-label classification\nEach line of the csv files indicates an image by its `id` and a list of corresponding `categories` present in the frame. \n```\nid, categories\n4a7f2199-772d-486d-b8e2-b651246316b5, [1.0]\n3bddedf6-4ff8-4e81-876a-564d2b03b364, \"[1.0, 9.0, 11.0, 88.0]\"\n3f735021-f5de-4168-b139-74bf2859d12a, \"[1.0, 37.0, 51.0, 119.0]\"\n130e185f-09c5-490c-8d08-641c4cbf6e54, \"[1.0, 51.0, 119.0]\"\n```\nThe `ids` correspond those in the object detection files. The `categories` are the set of all unique annotations found in the associated image.\n\n### Object detection\nThe datasets are formatted to adhere to the [COCO Object Detection](https://cocodataset.org/#format-data) standard. Every training image contains at least one annotation corresponding to a `category_id` ranging from 1 to 290 and a `supercategory` from 1 to 20. The fine-grained annotations are taxonomic thought not always at the same level of the taxonomic tree. \n\n### Supercategories\nEach category also belongs to one of 20 semantic supercategories as indicated in `category_key.csv`:\n```\n['Worm', 'Feather star', 'Sea cucumber', 'Squat lobster', 'Fish', 'Soft coral', 'Urchin', 'Sea star', 'Sea fan', 'Sea pen', 'Barnacle', 'Eel', 'Glass sponge', 'Shrimp', 'Black coral', 'Anemone', 'Sea spider', 'Gastropod', 'Crab', 'Stony coral']\n```\nThese supercategories might be useful for certain training procedures. The supercategories *are* represented in both the training and validation set. But please note that submissions must be made identifying the 290 fine grained categories. \n\nWe are not able to provide images as a single downloadable archive due to [FathomNet's Terms of Use](https://fathomnet.org/fathomnet/#/license). Images should be downloaded using the indicated `coco_url` field in each of the annotation files. Participants can either write their own script or use the provided `download_images.py` python script. \n\n## Terms of Use\nBy downloading and using this dataset you are agreeing to FathomNet's data use policy. In particular:\n\n- The annotations are licensed under a Creative Commons Attribution-No Derivatives 4.0 International License. \n- The images are licensed under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 International License. Notwithstanding the foregoing, all of the images may be used for training and development of machine learning algorithms for commercial, academic, and government purposes.\n- Images and annotations are provided by the copyright holders and contributors \"as is\" and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed.\n- Please acknowledge FathomNet and MBARI when using images for publication or communication purposes regarding this competition. For all other uses of the images, users should contact the original copyright holder.\n\nFor more details please see the full [FathomNet Use Policy](https://fathomnet.org/fathomnet/#/license). \n\n## Files\n\n*   **multilabel_classification/train.csv** - csv list of training images and categories\n*   **object_detection/train.json** - the training images, annotations, and categories in COCO formatted json\n*   **object_detection/eval.json** - the evaluation images in COCO formatted json\n*   **sample_submission.csv** - a sample submission file in the correct format\n*   **category_key.csv** - key mapping numerical index to category and supercategory name\n* **demo_download.ipynb** - python notebook demonstrating download script\n*  **download_images.py** - python script to download imagery from FathomNet using COCO formatted json \n* **requirements.txt** - python requirements to run the download script\n\n## Download\nImages are made available for download by the unique URLs in the COCO formatted object detection annotation files. The download script can be run from the command line. To install the requirements, create a virtual environment running `python 3.9`:\n\n```\n$ conda create -n fgvc_test python=3.9 pip\n$ conda activate fgvc_test\n$ pip install -r requirements.txt\n```\n\nTo download the images, run the script from the command line: \n\n```\n$ python download_images.py [PATH/TO/DATASET.json] --output [PATH/TO/IMAGE/DIRECTORY]\n```\n\nIf no `--output` directory is specified, the script by default downloads images to the directory the command is executed from. ",
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      "name": "Description",
      "content": "Ocean-going camera systems have given scientists access to amazing data products that allow them to monitor populations and discover new animals. Many groups have had success training and deploying machine learning models to help sort incoming visual data. But these models typically do not generalize well to new situations -- different cameras or illumination, new organisms appearing, changes in the appearance  of the seafloor -- an especially vexing problem in a dynamic ocean. Improving the robustness of these tools will allow marine ecologists to better leverage existing data and provide engineers with the ability to deploy instruments in ever more remote parts of the ocean. \n\nFor this competition, we have selected data from the broader [FathomNet](https://fathomnet.org/fathomnet/#/) annotated image set that represents a challenging use-case: the training set is collected in the upper ocean (< 800 m) while the target data is collected from deeper waters. This is a common scenario in ocean research: deeper waters are more difficult to access and typically more annotated data is available closer to the surface. The species distributions are often overlapping, but not identical, and diverge as the vertical distance between the samples increases. The challenge is both to identify animals in a target image and assess if the image is from a different distribution relative to the training data. Such out-of-sample detection could help scientists discover new animals and improve ecosystem management practices.  \n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3258%2F806da28465b4f5fc843dc6d1421784cc%2Ffathomnet_image.jpg?generation=1679607795591616&alt=media)\n\nThe images for both the training and target data were collected by a single camera system deployed by the [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) (MBARI) on several of its [Remotely Operated Vehicles](https://www.mbari.org/our-work/technologies/vehicles/?visc-qf-vehicle_tax_filter_technology_type%5B%5D=remotely-operated-vehicle-rov) off the coast of Central California. We encourage participants to leverage outside data sources as they see fit. Teams should plan on specifying additional data sources and/or pretrained models used when uploading results. Pretrained models can be used for initialization of training (e.g. ImageNet or COCO classification or detection models provided by many deep learning packages).\n\n## Competition information\nThis competition is presented as part of the [10th Fine-Grained Visual Categorization](https://sites.google.com/view/fgvc10/home) (FGVC10) workshp at [CVPR 2023](https://cvpr.thecvf.com/Conferences/2023) in Vancouver, Canada.  \n\nPlease feel free to open an issue on the [competition git repo](https://github.com/fathomnet/fgvc-comp-2023) if you have a question, comment, or problem. We will also respond to threads opened on the competition discussion board here. \n\n## Acknowledgements\nThe images for this competition has been generously provided by [MBARI](https://www.mbari.org/). Annotations were produced by the experts in the [MBARI Video Lab](https://doi.org/10.1109/OCEANS.2006.306879).",
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      "name": "Evaluation",
      "content": "For each image in the test set, you will predict a list of *categories* belonging to the image and classify whether the image is *out-of-sample*.\n\nThe out-of-sample predictions are evaluated by the [AUCROC](https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc) score, rescaled as follows:\n\n$$sAUC = 2 AUC - 1$$\n\nPredictions of categories are evaluated according to the Mean Average Precision at 20 (MAP@20):\n\n$$MAP@20 = \\frac{1}{U} \\sum\\_{u=1}^{U} \\sum\\_{k=1}^{min(n,20)} P(k) \\times rel(k)$$\n\nwhere \\\\( U \\\\)  is the number of images, \\\\( P(k) \\\\) is the precision at cutoff \\\\( k \\\\), \\\\( n \\\\) is the number predictions per image, and \\\\( rel(k) \\\\) is an indicator function equaling 1 if the item at rank \\\\( k \\\\) is a relevant (correct) label, zero otherwise.\n\nOnce a correct label has been scored for *an observation*, that label is no longer considered relevant for that observation, and additional predictions of that label are skipped in the calculation. For example, if the correct label is `1` for an observation, the following predictions all score an average precision of `1.0`.\n\n    [1, 2, 3, 4, 5]\n    [1, 1, 1, 1, 1]\n    [1, 2, 1, 3, 1]\n\nThe final score is the simple average:\n\n$$\\frac{1}{2} \\left( sAUC + MAP@20 \\right)$$\n\nYou can find a Python implementation of the metric in this notebook: [FathomNet metric](https://www.kaggle.com/code/ryanholbrook/fathomnet-metric/notebook).\n\n## Submission Format\nEntries should be submitted as a csv file with each line representing a single image. The image uuid should be followed by all detected `categories` and an `osd` score:\n\n```\nid,categories,osd\n8119e2ac-ca3a-4c3b-9e1c-c7a079a705c8,1 146 10 12 44 210,0.1\n11e2891-93a3-4532-a4ea-6e22e335ae54,17 82 251,0.9\n...\n```\n\nThe `id` is the image's `file_name` as indicated in the annotation files. The `categories` should be a space-delimited list of integer codes ordered by the probability the category is present in the image; this list will be automatically truncated after 20 entries. The `osd` field should be a probability or confidence score of whether the image is drawn from a different distribution relative to the training set.",
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      "name": "rules",
      "content": "\n\n\n\n\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 is 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 merge date. 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\nMaximum team size is 5\n\n## Submission Limits\n\nYou may submit a maximum of 5 entries per day. You 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\nCompetition Name (the 'Competition'): FathomNet 2023\n\nCompetition Sponsor: Monterey Bay Aquarium Research Institute (“MBARI”),\n\nCompetition Acknowledgements: Eric Orenstein (MBARI; [https://www.mbari.org/](https://www.mbari.org/)), Geneviève Patterson (OVAI; https://genp.github.io/ ), Ben Woodward (CVisionAI; https://www.cvisionai.com/ ), Kakani Katija (MBARI; https://www.mbari.org/ )\n\n  \n\nCompetition Website: https://www.kaggle.com/competitions/fathomnet-out-of-sample-detection and [FGVC10](https://sites.google.com/view/fgvc10/competitions/fathomnet2023)\n\n\n\n  \n\nWINNER LICENSE TYPE: Open Source\n\nDATA ACCESS AND USE: Competition and Academic Research\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, or 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\nENTRY 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:\n\n### 1. WINNERS LICENSE.\n\nUnder Section 11 (Winners Obligations) of the General Competition Rules below, you hereby grant and will grant the following license(s) with respect to your Submission if you are a Competition winner:\n\nOpen Source: You hereby license and will license your winning Submission and the source code used to generate the Submission under an Open Source Initiative-approved license (see [www.opensource.org](http://www.opensource.org/)) that in no event limits commercial use of such code or model containing or depending on such code. To the extent your Submission makes use of generally commercially available software not owned by you that you used to generate your submission, but that can be procured by the Competition Sponsor without undue expense, you do not grant the license in the preceding sentence to that software.\n\n  \n\n## 2. GUIDELINES\n\nCompetitors are expected to only use the training images during model training; validation images are only to be used to assess model performance. Pretrained models can be used for initializing training (e.g. ImageNet or COCO classification or detection models provided by many deep learning packages). We do encourage participants to leverage outside data sources as they see fit. Teams should plan on specifying additional data sources and/or pretrained models used when uploading results.\n\n## 3. COMPETITION DATA\n\nIn addition to Section 7 of these rules, you agree that by downloading and using this dataset you are agreeing to FathomNet's data use policy. In particular:\n\n-   The annotations are licensed under a Creative Commons Attribution-No Derivatives 4.0 International License.\n    \n-   The images are licensed under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 International.\n    \n-   Images and annotations are provided by the copyright holders and contributors \"as is\" and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed.\n    \n-   Please acknowledge FathomNet and MBARI when using images for publication or communication purposes.\n    \n\nFor more details please see the full [FathomNet Use Policy](https://fathomnet.org/fathomnet/#/license).\n\n  \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### 5. TRAINING DATA, PRETRAINED MODELS, AND ADDITIONAL ANNOTATIONS\n\nAll submissions must follow the rules defined in Section 7 regarding training data, use of pretrained models, and additional annotation. Any additional annotations collected on the allowed training data must be shared to all competitors. ANY AND ALL HAND LABELING OF THE TEST DATA IS EXPRESSLY FORBIDDEN.\n\n## B. GENERAL COMPETITION RULES\n\n### 1. BINDING 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. ELIGIBILITY.\n\nA. To be eligible to enter the Competition, you must be:  \n(i) a registered account holder at Kaggle.com;  \n(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(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(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)](https://www.treasury.gov/resource-center/sanctions/Programs/Pages/Programs.aspx)).\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 organizers, 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. SPONSOR 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](http://www.kaggle.com/terms) in addition to these Rules.\n\n### 4. COMPETITION PERIOD.\n\nFor the purposes of Prizes, the Competition will run from the Start Date 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. COMPETITION ENTRY.\n\nNO PURCHASE NECESSARY TO ENTER OR WIN.  \nTo 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. INDIVIDUALS 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. COMPETITION 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\nCompetition Use and Non-Commercial & Academic Research: 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\nPlease acknowledge FathomNet and MBARI when using images for publication or communication purposes. For more details please see the full [FathomNet Use Policy](https://fathomnet.org/fathomnet/#/license).\n\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. The general rule is that participants should only use the provided training and validation images for training models to classify the test images. We do not want participants crawling the web in search of additional data or using previous versions of this dataset. Pretrained models may be used to construct the algorithms from publicly available academic datasets (e.g. ImageNet, iNaturalist 2017-2018, Herbarium 2020). Please specify any and all external data and/or models used for training when uploading results.\n\nParticipants are allowed to collect additional annotations on the provided training sets. Participants are not allowed to collect annotations on the test set. Teams should specify that they collected additional annotations when submitting results.\n\n### 8. SUBMISSION 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](http://www.opensource.org/)) 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](http://www.opensource.org/)) that in no event limits commercial use of such code or model containing or depending on such code.\n\n### 9. DETERMINING 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. NOTIFICATION 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. WINNERS 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, 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. PRIZES.\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. TAXES.\n\nALL TAXES IMPOSED ON PRIZES ARE THE SOLE RESPONSIBILITY OF THE WINNERS.  \nPayments 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. GENERAL CONDITIONS.\n\nAll federal, state, provincial and local laws and regulations apply.\n\n### 15. PUBLICITY.\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. PRIVACY.\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](http://www.kaggle.com/privacy), 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](http://www.kaggle.com/contact).\n\nAs 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. WARRANTY, 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. INTERNET.\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. RIGHT 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. NOT 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. GOVERNING 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 Monterey, CA, 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.",
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      "content": "* __March 23, 2023__ - Competition start date.\n* __May 16, 2023__ - Entry deadline. You must accept the competition rules before this date in order to compete.\n* __May 16, 2023__ - Team Merger deadline. This is the last day participants may join or merge teams.\n* __May 23, 2023__ - Final submission deadline.\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.",
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    {
      "name": "CVPR 2023",
      "content": "This competition is part of the Fine-Grained Visual Categorization [FGVC10](https://sites.google.com/view/fgvc10) workshop at the Computer Vision and Pattern Recognition Conference [CVPR 2023](http://cvpr2023.thecvf.com/). A panel will review the top submissions for the competition based on the description of the methods provided. From this, a subset may be invited to present their results 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\nThere is no cash prize for this competition. PLEASE NOTE: CVPR frequently sells out early, we cannot guarantee CVPR registration after the competition's end. If you are interested in attending, please plan ahead.\n\nYou can see a list of all of the FGVC10 competitions [here](https://sites.google.com/view/fgvc10/competitions?authuser=0).",
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