Traffic sign classification web app

Implemented CNN for traffic sign classification using keras (link to Google Colab notebook). Dataset on which the model was trained.

The model recognizes following traffic sign classes:

1. Speed limit [20]
2. Speed limit [30]
3. Speed limit [50]
4. Speed limit [60]
5. Speed limit [70]
6. Speed limit [80]
7. End of speed limit [80]
8. Speed limit [100]
9. Speed limit [120]
10. No passing
11. No trucks passing
12. Right-of-way at intersection
13. Priority road
14. Yield
15. Stop
16. No vehicles
17. No trucks
18. No entry
19. General caution
20. Dangerous curve left
21. Dangerous curve right
22. Double curve
23. Bumpy road
24. Slippery road
25. Road narrows on the right
26. Road work
27. Traffic signals
28. Pedestrians
29. Children crossing
30. Bicycles crossing
31. Beware of ice/snow
32. Wild animals crossing
33. Speed and passing limits end
34. Turn right ahead
35. Turn left ahead
36. Ahead only
37. Go straight or right
38. Go straight or left
39. Keep right
40. Keep left
41. Roundabout
42. End of no passing
43. End of no passing trucks

How to run locally

# Cloning the repository
git clone https://github.com/ernurator/ML-Final-2022
cd ML-Final-2022

# Skip if virtualenv is installed
python3 -m pip install virtualenv

# Create virtual env
python3 -m virtualenv .venv
source .venv/bin/activate  # for Linux & MacOS

# Install all required libraries
pip install -r requirements.txt

# Run the web app
flask run
# Then open http://127.0.0.1:5000 in browser