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      "content": "The Forest Fire Dataset for Binary Classification is a comprehensive, multi-source image collection designed specifically to train and evaluate machine learning models for wildfire and forest fire detection. It provides a clean, organized, and labeled dataset ideal for binary classification tasks, helping participants build reliable and efficient models for real-world deployment.\n\nThe dataset is structured into two primary classes:\n\nFire: Images containing visible fire, flames, or smoke indicating active or potential wildfire.\n\nNoFire: Images representing forested or natural landscapes without any visible fire or smoke.\n\nEach image varies in size, resolution, and lighting conditions to ensure diversity and realism, reflecting the wide range of scenarios that occur in natural environments. The dataset supports experimentation with both traditional computer vision and deep learning-based approaches.\n\n🗂 Dataset Structure\n\nThe dataset is divided into three subsets to support effective model training and evaluation:\n\nTrain (70%) – Used for training the model and learning key visual patterns.\n\nValidation (10%) – Used for fine-tuning hyperparameters and avoiding overfitting.\n\nTest (20%) – Used to evaluate final model performance and generalization capability.\n\nEach subset contains separate folders for the fire and nofire categories:\ndataset/\n│\n├── train/\n│   ├── fire/\n│   └── nofire/\n│\n├── val/\n│   ├── fire/\n│   └── nofire/\n│\n└── test/\n    ├── fire/\n    └── nofire/\nData Collection and Preparation\n\nThis dataset was carefully curated by aggregating and cleaning images from multiple reliable sources to ensure quality and balance. The preparation process involved three major phases:\n\nAggregation:\nMultiple open-access forest fire datasets, academic repositories, and publicly available web sources were combined to create a unified dataset.\n\nManual Classification:\nA large collection of previously unlabeled images was manually reviewed and categorized into fire and nofire classes. This manual step greatly expanded the dataset’s diversity and ensured labeling accuracy.\n\nFinal Split:\nThe cleaned dataset was randomly divided into training, validation, and test sets (70/10/20 ratio) to maintain class balance and reproducibility.\n\nUsage Guidelines for Participants\n\nParticipants are encouraged to:\n\nDownload and explore the dataset freely.\n\nPreprocess, shuffle, or augment the data as they find appropriate for their model’s performance.\n\nPublish modified versions (if any) with proper documentation and credit to the original dataset.\n\nClearly describe all data preprocessing and augmentation steps in their submission notebook under the “Data Preparation” section.\n\nAll images in this dataset are sourced from publicly available and academic datasets. The combined collection has been cleaned and labeled solely for educational and research purposes as part of the EdgeFire Challenge 2025.",
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      "content": "The EdgeFire Challenge 2025 is a beginner-level AI competition focused on real-time forest fire detection using lightweight, edge-deployable machine learning models. Participants will design, train, and evaluate models capable of identifying forest fires from visual or sensor-based data while meeting the constraints of low-power hardware like Raspberry Pi, Jetson Nano, and similar edge devices.\n\nYour task is to create a novel yet efficient model that can:\n\nAccurately detect fire or smoke in real-time images or sensor readings.\n\nRun smoothly on limited hardware without relying heavily on cloud computation.\n\nMaintain a small model size and minimal inference latency.\n\nYou may use any open-source framework (TensorFlow Lite, PyTorch Mobile, ONNX, etc.) and can apply optimization techniques such as:\n\nQuantization (to reduce precision and improve inference speed)\n\nPruning (to remove redundant parameters)\n\nKnowledge distillation (to create smaller student models from large ones)\n\nCustom architecture design (for creativity and novelty)\n\nA Forest Fire Detection Dataset is provided. However, participants are free to:\n\nDownload and preprocess the dataset as they see fit.\n\nShuffle, split, or augment the data for better performance.\n\nCreate and publish a modified public version (with credits and documentation).\n\nClearly specify all dataset preprocessing steps in their submitted notebook/report under a section titled “Data Preparation.”\n\nThis competition aims to inspire new developers and students to explore AI for environmental protection, blending innovation, technical skill, and sustainability.",
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      "name": "Evaluation",
      "content": "Submissions will be evaluated based on a multi-factor scoring system that measures both technical performance and practical efficiency. Each model will be scored on the following criteria:\n\nCriteria\tDescription\tWeight (%)\nAccuracy / F1 Score\tModel’s ability to correctly detect forest fire vs. non-fire instances.\t35%\nModel Efficiency\tInference speed, memory footprint, and deployability on low-power edge devices.\t25%\nNovelty / Creativity\tOriginality of the approach, use of optimization techniques, or architectural innovation.\t20%\nReproducibility\tClarity and completeness of code, documentation, and dataset usage description in the notebook.\t10%\nEnvironmental Relevance\tHow effectively the model can be used in real-world forest fire prevention systems.\t10%\n⚖️ Bonus Considerations\n\nModels that successfully quantize or compress without major accuracy loss will receive extra credit.\n\nSubmissions including a working edge deployment demo (e.g., tested on Raspberry Pi, Jetson Nano, or similar) will gain bonus recognition points.\n\nParticipants who publicly share their improved dataset version with proper metadata and credits will be highlighted in post-competition results.",
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      "name": "rules",
      "content": "🏆 EDGEFIRE CHALLENGE 2025 — OFFICIAL COMPETITION RULES\nENTRY IN THIS COMPETITION CONSTITUTES YOUR ACCEPTANCE OF THESE OFFICIAL COMPETITION RULES.\n\nSee Section 3.18 for defined terms\n\nThe EdgeFire Challenge 2025 is a skills-based competition designed to promote innovation in Edge AI and Environmental Monitoring. You must register via the Competition Website to enter. To participate, you must agree to these Official Competition Rules, which include all terms, content, and policies referenced on the Competition Website and within these Rules (collectively, the “Rules”). By submitting an entry, you agree to be bound by these Rules. You may not submit to the Competition and are not eligible for any prizes unless you agree to these Rules. These Rules form a binding legal agreement between you and the Competition Sponsor.\n\n1. COMPETITION-SPECIFIC TERMS\n1. Competition Title\n\nEdgeFire Challenge 2025 — Edge-Friendly Forest Fire Detection\n\n2. Competition Sponsor\n\nNational Center of Artificial Intelligence (NCAI), NUST University\nin collaboration with ConservAi Research Initiative\n\n3. Competition Sponsor Address\n\nNational Center of Artificial Intelligence (NCAI),\nNUST Campus, H-12, Islamabad, Pakistan\n\n4. Competition Website\n\nhttps://www.kaggle.com/competitions/edgefire-challenge-2025\n\n5. Total Prizes Available:\n\n$600 (Equivalent in PKR)\n\n🥇 First Prize: $300\n\n🥈 Second Prize: $200\n\n🥉 Third Prize: $100\n(Non-monetary recognition certificates and publication opportunities for Top 10 teams)\n\n6. Winner License Type\n\nOpen Source License (MIT License)\n\n7. Data Access and Use\n\nCompetition Use and Non-Commercial & Academic Research\nParticipants may access and use the dataset for non-commercial, educational, and research purposes, as described below.\n\n2. COMPETITION-SPECIFIC RULES\n1. TEAM LIMITS\n\na. Maximum team size is five (5) members.\nb. Team mergers are allowed before the Team Merger Deadline, provided the merged team’s total submission count remains within the competition limit.\n\n2. SUBMISSION LIMITS\n\na. Each team may submit up to five (5) submissions per day.\nb. Teams may select up to two (2) final submissions for evaluation.\n\n3. COMPETITION TIMELINE\n\nAll official competition dates (Start Date, Entry Deadline, Final Submission Deadline, and Team Merger Deadline) are available on the Timeline page of the competition website.\n\n4. COMPETITION DATA\na. Data Access and Use\n\nThe dataset provided for this competition is the Forest Fire Dataset for Binary Classification, consisting of labeled fire and nofire image data.\nParticipants are permitted to:\n\nDownload and use the dataset for model development and submission.\n\nShuffle, preprocess, or augment the data to improve model performance.\n\nCreate and publish modified public versions of the dataset for research purposes, provided proper credit is given to the original dataset and changes are documented.\n\nUse the dataset solely for non-commercial, educational, and academic research purposes.\n\nNote: Redistribution or sale of the dataset for commercial purposes is strictly prohibited.\n\nb. Data Security\n\nParticipants must:\n\nEnsure that only authorized team members have access to the dataset.\n\nPrevent unauthorized duplication or sharing of the dataset.\n\nNotify the organizers immediately of any data misuse or breach.\n\n5. WINNER LICENSE\n\nAll winning submissions will be released under the MIT Open Source License, allowing open use for academic and research purposes.\nBy submitting, you agree that:\n\nYour source code and model architecture may be publicly shared for educational transparency.\n\nThe organizers may use or demonstrate your model (with attribution) in environmental or academic applications.\n\n6. EXTERNAL DATA AND TOOLS\n\nParticipants may use external datasets and open-source pretrained models under these conditions:\n\nThe external data is publicly available, free to access, and equally accessible to all participants.\n\nAny external data or pretrained models used must be clearly documented in the submission notebook.\n\nThe use of large proprietary or paid datasets is not allowed unless approved by the organizers.\n\nAllowed Tools:\n\nTensorFlow, PyTorch, Keras, ONNX, Scikit-learn\n\nOptimization libraries for pruning, quantization, and model compression\n\nEdge deployment libraries such as TensorFlow Lite, OpenVINO, or PyTorch Mobile\n\nAutomated Machine Learning Tools (AutoML) may be used if the participant holds valid licenses and documents the workflow.\n\n7. ELIGIBILITY\n\nOpen to students, beginners, and AI enthusiasts worldwide aged 16 or older.\n\nEmployees, interns, or contractors of the Competition Sponsor or affiliated institutions may participate but are not eligible for prizes.\n\n8. WINNER’S OBLIGATIONS\n\nWinners must:\n\nSubmit their final model code, documentation, and preprocessing pipeline.\n\nProvide a reproducible notebook showing all data handling, training, and evaluation steps.\n\nLicense the winning solution under the MIT License.\n\nComplete all required forms (eligibility certification, tax forms if applicable).\n\nFailure to comply may result in disqualification.",
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      "name": "abstract",
      "content": "# 🏆 **EdgeFire Grand Challenge 2025: Next-Gen AI for Early Fire Detection on Edge Devices**\n\n---\n\n## 🔥 **Overview**\n\nThe **EdgeFire Grand Challenge 2025** invites AI researchers, students, and Kaggle competitors to design **novel, efficient deep learning models** for real-time fire and smoke detection on **resource-constrained devices** such as Raspberry Pi, Jetson Nano, Android NPUs, and mini edge computers.\n\nParticipants will explore **lightweight architectures**, **multi-modal fusion**, **privacy-aware learning**, and **domain robustness** to build the next generation of **edge-deployable fire detection systems** capable of saving lives and forests worldwide.\n\nThis competition emphasizes:\n- 🧠 Novelty in model design  \n- ⚙️ Efficiency on edge hardware  \n- 🧩 Robustness under real-world distortions  \n- 🚨 Early detection capability  \n\n---\n\n## 💡 **Challenge Theme**\n\n> “Design a novel, efficient, and explainable AI model for early fire and smoke detection that can run on low-power devices with high accuracy and low latency.”\n\n---\n\n## 🧠 **Objective**\n\nBuild and evaluate deep learning models capable of:\n- Detecting **fire, smoke, and early onset cues**\n- Performing well under **limited compute and memory**\n- Maintaining **robustness** under lighting, compression, and weather distortions\n- Supporting **quantization/pruning** for real-time edge inference\n\n---\n\n## 📊 **Dataset**\n\nParticipants will use the **Forest Fire Dataset for Binary Classification**, featuring:\n- Two classes: `fire` and `nofire`\n- Pre-split into **train**, **val**, and **test**\n- Multi-source, with diverse lighting and weather conditions\n\nThey may **augment**, **expand**, or **restructure** the dataset (as long as their process is documented).  \nSynthetic and weakly labeled data are allowed — provided they are **publicly reproducible**.\n\n---\n\n## 🧩 **Tracks**\n\n### 🥇 **Track 1: Novel Architectures**\n\nImplement or extend any of the following conceptual models — or propose your own:\n\n| 🧠 **Model Name** | 🎯 **Concept Focus** | 💡 **Key Innovation** |\n|--------------------|----------------------|------------------------|\n| **FireFormer-E** | Early smoke cues | Temporal transformer with onset regression and haze-aware attention |\n| **MultiFire-MM** | RGB + Thermal + Audio fusion | Tri-modal alignment with adaptive fusion and cross-modality voting |\n| **SmokeSeg-YOLO-Decouple** | Lightweight detection + segmentation | Dual-head YOLO with shape-aware focal Tversky loss |\n| **DR-FireNet** | Domain robustness | Frequency and style adapters for compression and lighting variation |\n| **Fed-Private-Fire** | Privacy & scale | Federated LoRA adapters + DP-SGD for CCTV deployment |\n| **EventFire** | Event-based low-latency detection | Spiking-inspired CNN for real-time grayscale/event data |\n\nParticipants can choose one, combine ideas, or propose an entirely **novel variant** — creativity is encouraged.\n\n---\n\n### 🥈 **Track 2: Edge Efficiency**\n\nDeploy and benchmark your best-performing model on **edge hardware simulators** or **real devices**:\n\n**Target Devices:**  \n- Raspberry Pi 5  \n- Jetson Nano  \n- Jetson Orin Nano  \n- Android NPU  \n- CPU-only MiniPC  \n\nDemonstrate model optimization via:\n- Quantization (QAT or post-training)\n- Pruning\n- TensorRT / TFLite conversion\n\n**You must submit:**\n- 🧠 The compressed model file  \n- ⚙️ An inference benchmark report (FPS, latency, power)  \n- 📉 The model size and FLOPs count  \n\n---\n\n## ⚙️ **Evaluation Metrics**\n\n| 🧮 **Metric** | 📖 **Description** | 🏋️ **Weight** |\n|----------------|-------------------|---------------|\n| **Accuracy / mAP** | Fire vs. NoFire detection accuracy | **30%** |\n| **Latency (ms/frame)** | Inference time on Raspberry Pi / Jetson | **25%** |\n| **Model Size (MB)** | Smaller is better | **15%** |\n| **Novelty & Technical Soundness** | Innovation in architecture or optimization | **15%** |\n| **Explainability & Reporting** | Visual outputs, overlays, interpretability | **10%** |\n| **Robustness** | Performance under JPEG, fog, night, or blur | **5%** |\n\n---\n\n## 🧪 **Submission Requirements**\n\nEach submission must include:\n- 📔 **Notebook (.ipynb)** — Training + inference workflow  \n- 🧠 **Model weights** — `.tflite`, `.onnx`, or `.pt` (max 100 MB)  \n- 📄 **Technical report (PDF)** — Short 2–4 pages explaining:  \n  - Architecture overview  \n  - Optimization techniques used  \n  - Hardware benchmarks  \n  - Visual examples / explainability results  \n- ⚡ **Inference log** showing model speed and power usage  \n- 🎥 *(Optional)* Edge hardware video demo (YouTube or Drive link)\n\n---\n\n## 📦 **Baseline & Benchmark**\n\nA simple CNN baseline (e.g., **MobileNetV2 with 65–70% accuracy**) will be provided as a **Benchmark Submission**.  \nYour model must outperform this benchmark in at least **one dimension** (accuracy, latency, or model size).\n\n---\n\n## 🏁 **Evaluation & Leaderboard**\n\n- **Public Leaderboard:** 40% test data (Public split from `solution.csv`)  \n- **Private Leaderboard:** Remaining 60% test data (revealed post-competition)  \n- 🏆 Winners determined by **Weighted Composite Score** (accuracy + efficiency + novelty)\n\n---\n\n## 🧾 **Evaluation Environment**\n\n- **OS:** Ubuntu 22.04 / Jetson Linux / Raspberry Pi OS  \n- **Frameworks:** PyTorch, TensorFlow, or ONNX  \n- **Evaluation Tools:**  \n  - `onnxruntime`  \n  - `tflite_runtime`  \n  - `torch.profiler`  \n  - Power measurement via `tegrastats` or simulated runtime  \n\n---\n\n## 🌱 **Encouraged Techniques**\n\n- Quantization-aware training  \n- Pruning and distillation  \n- Cross-modal alignment (RGB + thermal or audio)  \n- Frequency-domain robustness  \n- Federated privacy adaptation  \n- Spiking or temporal transformers  \n\n---\n\n## 🧭 **Explainability & Trust**\n\nModels must provide interpretable outputs, including:\n- 🔥 Smoke and flame overlays  \n- 🎯 Grad-CAM or feature visualizations  \n- 📈 Uncertainty or confidence maps  \n\n**Bonus points for:**\n- False-alarm review loop  \n- Operator dashboard mockup  \n\n---\n\n## 📜 **Rules Summary**\n\n- Teams up to **5 members**  \n- Original work only (no pre-trained commercial APIs)  \n- Code and model weights must be **reproducible**  \n- External data allowed if **publicly accessible** and **credited**  \n- Winning submissions released under **MIT License**\n\n---",
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    {
      "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:</h5>\n1. a registered account holder at Kaggle.com; </h6>\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); </h6>\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</h6>\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]).</h6>\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.</h5>\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.</h5>   \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.</h5>\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.</h5>\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.</h5>\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.</h5>\nb. Submissions may not use or incorporate information from hand labeling or human prediction of the validation dataset or test data records.</h5>\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.</h5>\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. </h5>\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. </h5>\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.</h5>\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. </h5>\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.</h5>\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.</h5>\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.</h5>\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.</h5>\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.</h5>\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.</h5>\n\n####8. NOTIFICATION OF WINNERS & DISQUALIFICATION\na. The potential winner(s) will be notified by email.</h5> \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.</h5>\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.</h5>\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.</h5>\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.</h5>\nf. The final leaderboard list will be publicly displayed at Kaggle.com. Determinations of Competition Sponsor are final and binding.</h5>\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. </h5>\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).</h5>\nc. A potential winner may decline to be nominated as a Competition winner in accordance with Section 3.8.</h5>\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. </h5>\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.</h5>\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.</h5>\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.</h5>\n\n####11. GENERAL CONDITIONS\na. All federal, state, provincial and local laws and regulations apply.</h5>\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.</h5>\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]>.</h5>\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.</h5>\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.</h5>\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.</h5>\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.</h5>\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.</h5>\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.</h5>\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.</h5>\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. </h5>\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.</h5>\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.</h5>\nd. A “Participant” or “Participant User” is an individual who participates in a competition by entering the competition and making a Submission.</h5>\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.</h5>\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.</h5>\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.</h5>\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.</h5>\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.</h5>\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",
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