## ENTRY IN THIS COMPETITION CONSTITUTES YOUR ACCEPTANCE OF THESE OFFICIAL COMPETITION RULES.

**[See Section 3.18 for defined terms](rules#18.-terms)**

*The Competition named below is a skills-based competition to promote and further the field of data science. You must register via the Competition Website to enter. To 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 herein (collectively, the "Rules"). Please read these Rules carefully before entry to ensure you understand and agree. You further agree that Submission in the Competition constitutes agreement to these Rules. You may not submit to the Competition and are not eligible to receive the prizes associated with this Competition unless you agree to these Rules. These Rules form a binding legal agreement between you and the Competition Sponsor with respect to the Competition. Your competition 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.*

**You cannot sign up to Kaggle from multiple accounts and therefore you cannot enter or submit from multiple accounts.**

---

### 1. COMPETITION-SPECIFIC TERMS
#### 1. COMPETITION TITLE
German Traffic Sign Recognition Challenge  

#### 2. COMPETITION SPONSOR
Self-hosted (Educational Purpose)  

#### 3. COMPETITION SPONSOR ADDRESS
N/A (Educational/Practice Competition)  

#### 4. COMPETITION WEBSITE
https://www.kaggle.com/competitions/german-traffic-sign-recognition  

#### 5. TOTAL PRIZES AVAILABLE
This is an **educational competition**, therefore no monetary prizes are awarded. Participants compete for leaderboard ranking and learning purposes only.  

#### 6. WINNER LICENSE TYPE
Open Source – Winning submissions must be shared under an [Open Source Initiative-approved license](http://www.opensource.org).  

#### 7. DATA ACCESS AND USE
Competition Use and Non-Commercial & Academic Research only. You may use the Competition Data for:
- Participating in this competition
- Sharing work in Kaggle notebooks and discussions
- Academic research and education  

---

### 2. COMPETITION-SPECIFIC RULES

#### 1. TEAM LIMITS
a. The maximum Team size is five (5).  
b. Team mergers are allowed and can be performed by the Team leader, provided the combined team submission count is within the allowed limit.  

#### 2. SUBMISSION LIMITS
a. You may submit a maximum of five (5) submissions per day.  
b. You may select up to two (2) final submissions for judging.  

#### 3. COMPETITION TIMELINE
Competition start, deadlines, and team merger deadline are shown on the **Timeline** page.  

#### 4. COMPETITION DATA
a. **Data Access and Use**: You may access and use the Competition Data for non-commercial purposes only, including participating in the Competition, Kaggle forums, academic research, and education.  

b. **Data Security**:  
- You agree not to redistribute or republish the dataset outside Kaggle.  
- Any suspected misuse of data must be reported to Kaggle immediately.  

#### 5. WINNER LICENSE
Submissions that achieve winning leaderboard positions must be open-sourced under an [OSI-approved license](http://www.opensource.org), ensuring reproducibility and availability to the community.  

#### 6. EXTERNAL DATA AND TOOLS
- External data is allowed if it is **publicly available to all participants at no cost**.  
- Pretrained models (e.g., ImageNet) are allowed.  
- Proprietary or paid datasets are not allowed.  

#### 7. ELIGIBILITY
This is an **open educational competition**. Employees of Kaggle or affiliated entities may participate for learning purposes but are not eligible for prizes.  

#### 8. WINNER’S OBLIGATIONS
Winners must:  
1. Provide complete source code and documentation to reproduce their solution.  
2. Clearly describe model architecture, preprocessing steps, hyperparameters, and training procedure.  
3. Share code under an open-source license.  

#### 9. GOVERNING LAW
This competition is for **educational purposes only**. No legal claims or liabilities are implied.