This is a traffic sign classifier trained using CNN in keras wit tensorflow as the backend. It is having 43 different classes. The index of the different classes are given below. The trainig is done on google colab platform.
0 = Speed limit (20km/h)
1 = Speed limit (30km/h)
2 = Speed limit (50km/h)
3 = Speed limit (60km/h)
4 = Speed limit (70km/h)
5 = Speed limit (80km/h)
6 = End of speed limit (80km/h)
7 = Speed limit (100km/h)
8 = Speed limit (120km/h)
9 = No passing
10 = No passing for vehicles over 3.5 metric tons
11 = Rightofway at the next intersection
12 = Priority road
13 = Yield
14 = Stop
15 = No vehicles
16 = Vehicles over 3.5 metric tons prohibited
17 = No entry
18 = General caution
19 = Dangerous curve to the left
20 = Dangerous curve to the right
21 = Double curve
22 = Bumpy road
23 = Slippery road
24 = Road narrows on the right
25 = Road work
26 = Traffic signals
27 = Pedestrians
28 = Children crossing
29 = Bicycles crossing
30 = Beware of ice/snow
31 = Wild animals crossing
32 = End of all speed and passing limits
33 = Turn right ahead
34 = Turn left ahead
35 = Ahead only
36 = Go straight or right
37 = Go straight or left
38 = Keep right
39 = Keep left
40 = Roundabout mandatory
41 = End of no passing
42 = End of no passing by vehicles over 3.5 metric tons
Train loss : 0.93 Train accuracy : 0.99
Validation loss : 2.94 Validation accuracy : 0.98
Test loss : 14.54 Test accuracy : 0.95
- Google colaboratory
- tensorflow 2.0
Step 1: Download the data set
Download the dataset and upload to the google drive from here
It is already having three .p files of 32x32 resized images:
- train.p: The training set.
- test.p: The testing set.
- valid.p: The validation set.
We will use Python pickle to load the data.
Step 2: Run the jupyter notebook
upload and run all the cells of the jupyter notebook in google colab.