A traffic sign classifier for self-driving cars using CNN and FNN, with modified Lenet architecture, touching a modest 82.1% accuracy on test data.
- Import images from the dataset.
- Preprocessing of images, such as shuffling, greyscaling, histogram equilization, and normalisation.
- Runnibg CNN and FNN for training.
- Running the session to begin the training
- testing model accuracy by testing through a random image.
- The accuracy on training and testing set was very poor even on applying various types of image preprocessing.
- The image uploading turned out to be tricky if not uploaded within a dedicated time span of running cell.
- To significantly increase accuracy, introduce third and forth arguments 'mean' and 'stddev' in both CNN and FNN, where mean = 0 and stddev = 0.1
- To avoid jumbling paths in Drive, upload the image while the cell is still running, this will ensure that the file is stored in correct folder in Google Drive.
- It can be downloaded into your local pc at https://d17h27t6h515a5.cloudfront.net/topher/2017/February/5898cd6f_traffic-signs-data/traffic-signs-data.zip
- The dataset has a total of 43 classes with each number associated with a specific class.
- The training set composed of 37499 images, while the test set totalled to 12630 images.
- All images are 32 by 32 in size.
The training set reached an accuracy of 91.7 % with test set reaching a decent 82.1 %
Add multiple hidden layers so as to increase the efficiency. A proper observation of VGG 16 or Lenet architecture may also enhance the accuracy of the result.