/traffic-signs-classification

A Neural Net for classifying traffic signs trained on GTSRB dataset.

Primary LanguagePython

Neural Network for classification of traffics signs.

Dataset Used: GTSRB Dataset (http://benchmark.ini.rub.de/?section=gtsrb&subsection=news).

Model Summary:

Model: "sequential"
Test Set Accuracy: 0.9908
Loss: 0.0833

Layer (type) Output Shape Param #
conv2d (Conv2D) None, 28, 28, 32) 896
max_pooling2d (MaxPooling2D) (None, 14, 14, 32) 0
flatten (Flatten) (None, 6272) 0
dense (Dense) (None, 128) 802944
dropout (Dropout) (None, 128) 0
dense_1 (Dense) (None, 43) 5547

Total params: 809,389
Trainable params: 809,387
Non-trainable params: 0

Usage:

To retrain the model:

python traffic.py data_directory [model.h5]

To make predictions:

python recognition.py