정리

• Udacity 프로젝트 페이지
  • https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/614d4728-0fad-4c9d-a6c3-23227aef8f66/project#
• 학습 데이터는 구글 드라이브에 있음
  • https://drive.google.com/drive/folders/1jIawlIMXY89wDLcV_E0xFX_ARjPR1VMo
• 프로젝트 제출 리뷰
  • https://review.udacity.com/?utm\_medium=email&utm\_campaign=ret\_000\_auto\_ndxxx\_submission-reviewed&utm\_source=blueshift&utm\_content=reviewsapp-submission-reviewed&bsft\_clkid=e7d35944-2468-42de-a204-b688914e8d08&bsft\_uid=7f3c83f7-9c33-42e7-8342-e90274359560&bsft\_mid=f207cb57-9c35-4b20-bd60-38650a164743&bsft\_eid=6f154690-7543-4582-9be7-e397af208dbd&bsft\_txnid=fc5b3ad2-6349-413a-bbd4-2f0e27f75944\#!/reviews/1090911

• AWS Instance

• i-0435a514433ce1f0d, Elastic IP 연결

• ssh [email protected]

• • source activate carnd-term1\
  • Launch the Jupyter notebook: jupyter notebook Traffic_Sign_Classifier.ipynb
• 여기의 CarND-Term1/CarND-Traffic-Sign-Classifier 폴더 바로 실행 가능함.

관련 자료

• 이미지를 조작하여 데이터를 더 만들어 내용 방법등
  • https://navoshta.com/traffic-signs-classification/
• Jeremy Shannon 프로젝트
  • https://github.com/jeremy-shannon/CarND-Traffic-Sign-Classifier-Project
• 김하영님 프로젝트
  • https://github.com/Hayoung-Kim/Perception-for-Self-driving/blob/master/Traffic-Sign-Classifier/Traffic_Sign_Classifier.ipynb
  • Confusion Matrix등 참고할 게 있음.
• 기타 참고했던 프로젝트
  • https://github.com/eqbal/CarND-Traffic-Sign-Classifier-Project/blob/master/Traffic_Sign_Classifier.ipynb