This is used for creating a model and predicting traffic signs, based on the GTSRB (German Traffic Sign Recognition Benchmark) Dataset.
The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011. We cordially invite researchers from relevant fields to participate: The competition is designed to allow for participation without special domain knowledge. Our benchmark has the following properties:
- Single-image, multi-class classification problem
- More than 40 classes
- More than 50,000 images in total
- Large, lifelike database
pip -r install requirements.txt
Once done create a folder inside named training_data and inside that create 2 folders train and val.
Update the paths in Source Code.
Dataset: GTSRB (German Traffic Sign Recognition Benchmark)
Preprocessing : First the data was divided with a split_size of 0.1 using split_data() and test set was ordered using Test.Csv from the dataset with order_test_set() located in my_utils.py
CONVOLUTIONAL LAYER
MAXPOOL LAYER
BATCH NORMALIZATION LAYER CONVOLUTIONAL LAYER
MAXPOOL LAYER
BATCH NORMALIZATION LAYER
CONVOLUTIONAL LAYER
MAXPOOL LAYER
BATCH NORMALIZATION LAYER
FLATTEN LAYER
DENSE LAYER
DENSE LAYER
Training and Testing:
Run GTSRB.py, with TRAIN = True and TEST = True
Predicting Single Image:
Run my_predictor.py after updating the image path with the path of the image to be predicted.
Note in windows folders are numbered in the following sequence,
0,1,10,11,12,13,14,15,16,17,18,19,2,20,21,22,23,24,25,26,27,28,29,3,30,31,32,33,34 ...
If you want to run mnist_example.py, just execute the file. The dataset will be downloaded automatically when running for the first time.
QUICK UPDATE: We were able to train and classify traffic lights(customly added to dataset) and signs which are not there in the dataset