{ "fetched_at": "2026-10-19T07:23:34Z", "source_url": "https://www.kaggle.com/competitions/grocery-items-multi-class-object-detection/data", "pages": [ { "name": "data-description", "content": "# Dataset Description\n\n## 📁 Files Provided\n\n```plaintext\n/Starter_Dataset\n├── /testImages/ # Real-world test images (you'll be judged on)\n│ └── /images/ \n├── /Dataset/ # Synthetic data to start training, create more on FalconCloud\n│ ├── /train/\n│ │ ├── /images/ \n│ │ └── /labels/\n│ └── /val/ #Synthetic and real-world data for validation, create more synthetic on FalconCloud\n│ ├── /images/\n│ └── /labels/\n\n├── convert_preds_to_csv.py # Script to convert YOLO predictions to Kaggle CSV\n├── predict.py # YOLOv8 inference script\n├── yolo_params.yaml # Configuration file for training/inference\n```\n\n### 🔑 Key Files:\n- **Starter_Dataset.zip**: Includes synthetic training/validation data folders and the real-world test set.\n- Dataset contains:\n - `/train/images` and `/train/labels` – for training\n - `/val/images` and `/val/labels` – for validation # includes real-world data ONLY for validation, not training\n- `/TestImages/images`: Contains real-world test images used for scoring.\n- **classes.txt**: Defines class names (`cheerios`, `soup`).\n- **yolo_params.yaml**: Used by training and prediction scripts to set dataset paths and options.\n- **convert_preds_to_csv.py**: Converts YOLO prediction `.txt` files into Kaggle submission format.\n- **predict.py**: Creates the predictions for scoring\n\n---\n\n## 📊 Data Format\n\n### 1. **Images** \n- Format: `.jpg` or `.png` \n- Resolution: All synthetic images are `1920x1080px` , some portrait and some landscape\n- Located in `/images/` subfolder\n\n### 2. **Labels (YOLO Format)** \nEach image has a corresponding `.txt` file inside the matching `/labels/` folder. \nEach line in the file follows this format:\n- class_id confidence x_center y_center width height\n- Coordinates are **normalized** (0 to 1) relative to image dimensions \n- Example (`IMG_0001.txt`): \na. Single object: 0 0.95 0.45 0.50 0.20 0.15\nb. Two objects: 1 0.95 0.45 0.50 0.20 0.15 0 0.92 0.70 0.30 0.10 0.12\n\n### 3. **Class ID** \n- **class 0**: cheerios, **class 1**: soup , **class 2**: candles\n- There are no other object categories in this challenge.\n\n---\n\n## 💡**IMPORTANT!** \n \nThe **Scenario set up on Falcon Cloud** offers a way for you to generate a **richer set of high-variability synthetic images**, for helping **improve model generalization** and **real-world performance.** \n\nLeverage the tool and create your own targeted datasets for better results and a competitive edge on the leaderboard!\n\n📥 [Start generating your own data here!](https://falcon.duality.ai/secure/scenarios/edit/7c5b51d0-553c-4bd3-9566-73723bbc43a5)\n\n---\n\n## 🎯 What You're Predicting\nPredict **bounding boxes** for real-world test images in YOLO format: \n- csv file of all predictions with 2 major entries being, \"image_id\", \"prediction_string\" \n- Each line: `image_id\", \" object_class confidence x_center y_center width height\"` \n- **Confidence scores** must be between 0 and 1\n\n---\n\n## 📖 Acronyms\n| Term | Definition | \n|------|------------| \n| **YOLO** | You Only Look Once (object detection architecture) | \n| **mAP@50** | Mean Average Precision at 50% IoU | \n| **IoU** | Intersection over Union (box overlap metric) | \n\n---\n\n## ❓ Frequently Asked Questions\n\n**Q: Can I use external data for training?** \nA: Only if it's created using FalconEditor or FalconCloud. \n\n**Q: What image sizes and formats should I expect?** \nA: Synthetic images are 1980x1080px in .png format. Real test images will be 1920x1080px. \n\n**Q: How should I improve model performance and results beyond a certain point?** \nA: [Check out our tips and tricks page](https://falcon.duality.ai/secure/documentation/model-and-data-improvements?sidebarMode=learn?utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_fk_1) for improving performance through scenario changes, data manipulation, model tuning, and more!\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "## 🚀 **About This Challenge** \n####**What You Need to Do**\n\nParticipants will train a model on **synthetic images** generated by each individual on FalconCloud\n\n**Note: Something different from last competitions is that we have provided a small number real-world images for the validation. These are for validation ONLY, not training.**\n\nThen, the trained model will then accurately predict bounding boxes for **real-world images**. \nThe model’s performance is evaluated based on [**mAP@50**.](https://falcon.duality.ai/secure/documentation/ex3-colab-testing-output?sidebarMode=learn&utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_fk1#the-meaning-of-50-in-m-ap-50) \n\n---\n\n## **🛠 How to Get Started** \n[👉 Get started with FalconCloud](https://falcon.duality.ai/secure/scenarios/edit/7c5b51d0-553c-4bd3-9566-73723bbc43a5?utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_fk1)\n \n### Generate Initial Dataset(s)\n1. [Go to the competition's page on the FalconCloud website for links to various resources, including the data-generating scenarios](https://falcon.duality.ai/secure/documentation/kaggle-page?utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_fk1).\n2. **[Start generating data](https://falcon.duality.ai/secure/scenarios/edit/7c5b51d0-553c-4bd3-9566-73723bbc43a5?utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_fk1)** using your personally selected variations. \n3. **Train a model of your choice (YOLO recommended)**\n4. **Run predictions on test images** \n5. **Convert the labels in .txt to a submission.csv** with our `convert_to_csv.py` file and then submit the csv file.\n `python convert_preds_to_csv.py`\n6. **Confirm the format of your predictions** are correct using the submission format described in the Evaluation Section.\n\n### Analyze Model Weakness and Improve Datasets\n1. Analyze and address weakness by generating more robust and targeted data using:\n[The soup+ Multi-class data generating scenario.](https://falcon.duality.ai/secure/scenarios/edit/7c5b51d0-553c-4bd3-9566-73723bbc43a5?utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_fk1)\n\n---\n\n## **Workflow** \n\n1. Create a free Falcon account and run the FalconCloud Scenario\n2. Configure the parameters you think will generate the most aligned data\n - [You can reference this page](https://falcon.duality.ai/secure/documentation/model-and-data-improvements?sidebarMode=learn&utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_fk1) for tips on effective synthetic data creation.\n3. Generate multiple datasets using a variety of configurations, and identify dataset weaknesses\n - [Add occlusions](https://falcon.duality.ai/secure/documentation/colab-ex-1-whats-next?sidebarMode=learn&utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_fk1)\n - [Include similar twins for improving precision](https://falcon.duality.ai/secure/documentation/ex3-colab-whats-next?sidebarMode=learn&utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_fk1)\n - [Combine target datasets](https://falcon.duality.ai/secure/documentation/ex3-colab-whats-next?sidebarMode=learn&utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_fk1) \n4. **Train a model** \n - Local training instructions start on [this page](https://falcon.duality.ai/secure/documentation/ex3-training-installs?sidebarMode=learn&utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_fk1)\n - Google Colab cloud training instructions are on [this page](https://falcon.duality.ai/secure/documentation/ex3-colab-training-process?sidebarMode=learn&utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_fk1)\n5. **Run predictions on test images** \n - Local installation prediction instructions are on [this page](https://falcon.duality.ai/secure/documentation/ex3-test?sidebarMode=learn&utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_fk1)\n - Google Colab cloud prediction instructions are on [this page.](https://falcon.duality.ai/secure/documentation/ex3-colab-test?sidebarMode=learn&utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_fk1)\n6. **Convert the labels in .txt to a submission.csv** with our `convert_to_csv.py` file and then submit the csv file.\n ```sh\npython convert_preds_to_csv.py\n```\n7. **Confirm the format of your predictions** are correct using the submission format described in the Evaluation Section.\n8. Create more data to target model weaknesses, align the synthetic data domain with the real-world domain, and create more effective training data.\n9. Check the leaderboard to see how your model performs in comparison to other competitors!\n10. **Join the [Discord Community](https://discord.com/invite/dualityfalconcommunity)** for solving queries related to the competition in the `kaggle-competitions` channels\n\n### **Additional Option: Generate More Diverse Data Locally Using FalconEditor** \n1. **Sign up and download FalconEditor for free**: 👉 [Falcon EDU Account](https://falcon.duality.ai/secure/documentation/ex3-install?sidebarMode=learn&utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_fk1) \n2. Complete **Exercise 3** to access a much larger range of variety and control.\n - [Click here to view computer restrictions under the \"Support and Requirements\" section.](https://falcon.duality.ai/secure/documentation/intro-to-the-exercises?sidebarMode=learn&utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_fk1)\n - [Click here to get started with FalconEditor on your local computer!](https://falcon.duality.ai/secure/documentation/ex3-objdetection-multiclass?sidebarMode=learn&utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_fk1)\n3. Improve model performance.\n\n## 🎯 **Rules**\n📌 **General Rules**\n- **Teams**: Maximum **5 members** per team.\n- **Pretrained Models**: You may use pretrained models.\n- **Submission Limit**: 3 submissions per day.\n- **Data**: You may ONLY use synthetic data from Falcon to train your model, but we have included some real-world images for the validation.\n\n🚨 **Disqualifications**\n\nViolations include but are not limited to:\n- Manually modifying test labels.\n- Using real world data for training.\n\n🔗 **Leaderboard**\n- **Public Leaderboard**: Uses a **50%** of the test set for real-time rankings.\n- **Private Leaderboard**: Final ranking based on other **50%** hidden test labels.\n\n\n---\n\n## 📣 **Community & Support**\n- **Join Duality's** [Discord](https://discord.com/invite/dualityfalconcommunity) for Falcon help and troubleshooting. \n- **Follow us on LinkedIn** for updates and opportunities📢\n - [Duality AI](https://www.linkedin.com/company/dualityai/)\n\n\n\n💡 **Think you can build the best synthetic-to-real object detector? Let’s see what you've got!**\n\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "## 📈 **Evaluation Criteria**\nYour submission will be evaluated using **Mean Average Precision (mAP@50)**, a standard metric in object detection (check the sample_submission.csv for more clarity).\n\n### **What is mAP@50?**\n- **Mean Average Precision (mAP)** measures the accuracy of object detection models.\n- The **IoU (Intersection over Union) threshold is set at 0.5**.\n- Higher `mAP@50` means your model performs better in detecting objects.\n\n### [**How is Your Score Calculated?**](https://falcon.duality.ai/secure/documentation/ex2-colab-testing-output?sidebarMode=learn&utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_2)\n- **Predicted bounding boxes** compared to **ground truth labels**.\n - For **images with obstructions**, the bounding box should be placed over the **visible portion** of the soup can.\n- The **IoU threshold is set at 0.5**, meaning a detection is considered correct if it overlaps with the true object by at least **50%**.\n- Your final score is based on the **average precision across all test images**.\n\n---\n\n## 📥 **Submission Format**\nTo participate, submit your csv file with **predictions in YOLO format**. \n\nEach **test image** should have a corresponding \"image_id\" and a \"prediction_string\" with predicted bounding boxes. \n\n### **CSV File Format**\nEach prediction should contain predictions in **YOLO format**:\n\"image_id\", \"prediction string(object class class_confidence \n x_center y_center width height)\".\n\n- **image_id** → image name without the .jpg/.png extension\n- **Object class** → `0` for cheerios, `1` for soup\n- **confidence** → Prediction confidence (between `0` and `1`).\n- **x_center, y_center, width, height** → Coordinates normalized between `0` and `1`.\n\n#### **Example (`0002.png` for a single detected object)**\n\"0002\",\"0 0.9435517191886902 0.874369642469618 0.30810502370198567 0.25126071506076386 0.25204394658406576\"", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "rules", "content": "## ENTRY IN THIS COMPETITION CONSTITUTES YOUR ACCEPTANCE OF THESE OFFICIAL COMPETITION RULES.\n\n**[See Section 3.18 for defined terms](rules#18.-terms)**\n\n*The Competition named below is a skills-based competition to promote synthetic-to-real object detection. You must register via Kaggle to enter. By participating, you agree to these Rules and confirm your Submissions comply with all requirements.*\n\n**You cannot sign up to Kaggle from multiple accounts and therefore you cannot enter or submit from multiple accounts.**\n\n---\n\n### 1. COMPETITION-SPECIFIC TERMS\n

1. COMPETITION TITLE

Grocery Items: Multi-Class Object Detection \n

2. COMPETITION SPONSOR

Duality AI\n

3. COMPETITION WEBSITE

https://www.kaggle.com/competitions/grocery-items-multi-class-object-detection\n

6. DATA ACCESS AND USE

\n- Synthetic training data generated via Duality AI's Falcon may be used for **non-commercial use only**\n- Real-world test data is strictly for competition evaluation\n\n---\n\n### 2. COMPETITION-SPECIFIC RULES\n\n#### 1. TEAM LIMITS\na. Maximum team size: **5 members** \n\n#### 2. SUBMISSION LIMITS\na. **3 submissions/day** during validation phase \nb. **3 final submissions** for private leaderboard scoring\n\n#### 3. COMPETITION TIMELINE\nPer Kaggle's Timeline page\n\n#### 4. COMPETITION DATA\na. **Synthetic Data**: \n - May be used only for competition participation \n\nb. **Real-World Test Data**: \n - Strictly for generating competition submissions (excluding the real-world images GIVEN in the data validation folder)\n - Reverse engineering not allowed \n\nc. **Real world Training data**\n - Not allowed for training \n\nd **Real world data for Validation**\n - You may use only the provided real world validation data present in the val folder in the \"Data\" tab\n\n\n#### 6. EXTERNAL DATA & TOOLS\na. **Allowed**: \n - Pre-trained Object Detection models \n - Falcon-generated synthetic data \n\nb. **Prohibited**: \n - Proprietary datasets requiring paid licenses \n - Public datasets \n - Manually labeled test data \n - Pseudo Labelling\n\n#### 7. FALCON SYNTHETIC DATA\na. Participants will **need** to generate synthetic data using [Duality AI's FalconCloud](https://falcon.duality.ai/secure/scenarios/edit/7c5b51d0-553c-4bd3-9566-73723bbc43a5?utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_fk1) \n\nb. Falcon-generated data must be: \n - Shared publicly on Kaggle Discussions if used in submissions or on Community Discord \n - Created using free-EDU tier accounts \n\n#### 8. ELIGIBILITY\na. Duality AI/Kaggle employees may participate but cannot win prizes \nb. Participants must have legal right to use all submitted data", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "abstract", "content": "# Multi-Class Object Detection Challenge 🚀\n\n## 🏆 Challenge Summary\n\nWelcome to the continuation of [Duality AI](https://www.duality.ai)'s Synthetic-to-Real Object Detection Challenges - now with more classes!\n\nCheck out some of our previous (finished) competitions to see what we've been building:\n- [Multi-class Object Detection](https://www.kaggle.com/competitions/multi-class-object-detection-challenge)\n- [Multi-instance Object Detection](https://www.kaggle.com/competitions/multi-instance-object-detection-challenge)\n- [Synthetic2Real Object Detection Challenge 2](https://www.kaggle.com/competitions/synthetic-2-real-object-detection-challenge-2)\n- [Synthetic to Real Object Detection Challenge](https://www.kaggle.com/competitions/synthetic-2-real-object-detection-challenge)\n\n###Your Goal for this competition: \nBuild an object detection model that can identify **multiple classes** (soup + cheerios + candles), with variety within the classes (multiple types of candles). Train on synthetic data from the free FalconCloud simulation (the cloud version of Duality’s high-fidelity digital twin simulation platform), and test on **real-world test images**. \n\nSee how high quality digital twins with carefully crafted simulation can bridge the sim2real gap!\n\n---\n\n### Who is this competition for?\n\n**Learners wanting to develop skills in:**\n- Vision systems\n- Object detection\n- Data improvement/analysis\n- Synthetic data and simulation\n\n---\n\n### 🌟 What’s New in This Challenge?\n\n- **Three object classes**: Cheerios + Soup + Candle\n- [**Generate Data in FalconCloud**](https://falcon.duality.ai/secure/scenarios/edit/7c5b51d0-553c-4bd3-9566-73723bbc43a5?utm_source=kaggle&utm_medium=kaggle_competition&utm_campaign=competition_fk1): Generate data on the cloud, train, and tweak parameters to create better data for higher performance!\n\n---\n\n### 🧠 Learn the Real-World Workflow\n\nIn this challenge, you’ll follow a simulation-to-deployment pipeline used by actual AI engineering teams:\n\n1. **Generate synthetic training data** using FalconCloud \n2. **Train an object detection model** (YOLO recommended) \n3. **Analyze model performance and Identify weaknesses** in your model such as persistent false positives or false negatives\n4. **Collect more data** from the Falcon scenario to address those gaps \n5. **Repeat** the process to achieve the highest mAP@50\n\n\nThis mimics the workflow used in fields like robotics, manufacturing QA, and autonomous systems - where collecting real-world data is expensive and iterative simulation is key.\n\n---\n\n### 💡 Why This Challenge Matters\n\nSynthetic data is **scalable, bias-controllable, and cost-effective**, but it still requires domain alignment to perform in the real world. **Duality** has spent years building technology to bridge that **Sim2Real gap**, and now you get hands-on access to those tools - for free.\n\n---\n\nReady to build an object detector that adapts, learns, and generalizes - all from simulation? Let’s get started! \n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "foundational-rules", "content": "The following Kaggle Competition Foundational Rules (“ Foundational Rules ”) apply to every competition regardless of whether the Sponsor creates competition-specific rules. Any competition-specific rules provided by the Sponsor are in addition to these rules, and in the case of any conflict or inconsistency, these Foundational Rules control and nullify contrary competition-specific rules.\n###GENERAL COMPETITION RULES - BINDING AGREEMENT\n####1. ELIGIBILITY\na. To be eligible to enter the Competition, you must be:\n1. a registered account holder at Kaggle.com; \n2. 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); \n3. 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\n4. not a person or representative of an entity under U.S. export controls or sanctions (see: [https://www.treasury.gov/resourcecenter/sanctions/Programs/Pages/Programs.aspx][1]).\n\nb. Competitions 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 Host reserves the right to forego or award alternative Prizes where needed to comply with local laws. If a winner is located in a country where prizes cannot be awarded, then they are not eligible to receive a prize.\n\nc. If 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 where you are an employee. If you are acting within the scope of your employment, or as an agent of another party, you warrant that such party or your employer 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\nd. The 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\n####2. SPONSOR AND HOSTING PLATFORM\n\na. The 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####3. COMPETITION PERIOD\na. For 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####4. COMPETITION ENTRY\na. NO 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.\nb. Submissions may not use or incorporate information from hand labeling or human prediction of the validation dataset or test data records.\nc. If 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.\nd. Submissions 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####5. INDIVIDUALS AND TEAMS\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. \nb. Teams. If permitted under the Competition Website guidelines, multiple individuals may collaborate as 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.\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. \nd. Private Sharing. No private sharing outside of Teams. Privately 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####6. SUBMISSION CODE REQUIREMENTS\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.\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.\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####7. DETERMINING WINNERS\na. Each 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.\nb. In 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####8. NOTIFICATION OF WINNERS & DISQUALIFICATION\na. The potential winner(s) will be notified by email. \nb. If 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.\nc. In 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.\nd. Competition 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.\ne. A 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.\nf. The final leaderboard list will be publicly displayed at Kaggle.com. Determinations of Competition Sponsor are final and binding.\n\n####9. PRIZES\na. Prize(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. \nb. All 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).\nc. A potential winner may decline to be nominated as a Competition winner in accordance with Section 3.8.\nd. Potential 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 thirty (30) days after receipt by Competition Sponsor or Kaggle of the required Prize acceptance documents. Transfer or assignment of a Prize is not allowed. \ne. You are not eligible to receive any Prize if you do not meet the Eligibility requirements in Section 2.7 and Section 3.1 above.\nf. If 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####10. TAXES\na. ALL 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####11. GENERAL CONDITIONS\na. All federal, state, provincial and local laws and regulations apply.\n\n####12. PUBLICITY\na. You 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####13. PRIVACY\na. You 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]>.\nb. 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####14. WARRANTY, INDEMNITY AND RELEASE \na. You 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.\nb. To 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.\nc. You 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####15. INTERNET\na. Competition 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####16. RIGHT TO CANCEL, MODIFY OR DISQUALIFY\na. If 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####17. NOT AN OFFER OR CONTRACT OF EMPLOYMENT\na. Under 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####18. DEFINITIONS\na. \"Competition Data\" are 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. \nb. An “Entry” is when a Participant has joined, signed up, or accepted the rules of a competition. Entry is required to make a Submission to a competition.\nc. A “Final Submission” is the Submission selected by the user, or automatically selected by Kaggle in the event not selected by the user, that is/are used for final placement on the competition leaderboard.\nd. A “Participant” or “Participant User” is an individual who participates in a competition by entering the competition and making a Submission.\ne. The “Private Leaderboard” is a ranked display of Participants’ Submission scores against the private test set. The Private Leaderboard determines the final standing in the competition.\nf. The “Public Leaderboard” is a ranked display of Participants’ Submission scores against a representative sample of the test data. This leaderboard is visible throughout the competition.\ng. A “Sponsor” is responsible for hosting the competition, which includes but is not limited to providing the data for the competition, determining winners, and enforcing competition rules.\nh. A “Submission” is anything provided by the Participant to the Sponsor to be evaluated for competition purposes and determine leaderboard position. A Submission may be made as a model, notebook, prediction file, or other format as determined by the Sponsor.\ni. A “Team” is one or more Participants participating together in a Kaggle competition, by officially merging together as a Team within the competition platform.\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\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }