Valentyn Sichkar

PhD student

few words about me

An engineer in Control Systems and Robotics, PhD student, working on Computer Vision, Deep Learning, Image Processing, Autonomous Vehicles. Python, C#, HTML, YOLO, CNNs.

Write an email

Traffic Signs Classification

Traffic Signs Classification online with Convolutional Neural Networks and German Traffic Sign Recognition Benchmarks dataset. It is possible to upload image or to choose random image from test dataset.


Upload Image


or choose random image from test dataset

4811 runs

Traffic Sign Classification


  • Classification Model

    Model #1 is built on pure 'numpy' and reached 0.98 Validation Accuracy.
    Description of implementation with pure 'numpy' is available on GitHub here
    Code implemented on Keras is available on Kaggle here

    Related Paper:
    Sichkar V. N. Effect of various dimension convolutional layer filters on traffic sign classification accuracy. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2019, vol. 19, no. 3, pp. DOI: 10.17586/2226-1494-2019-19-3-546-552 (Full-text available here ResearchGate.net/profile/Valentyn_Sichkar)

    Related Course:
    Training YOLO v3 for Objects Detection with Custom Data. Build your own detector by labelling, training and testing on image, video and in real time with camera. Available here: https://www.udemy.com/course/training-yolo-v3-for-objects-detection-with-custom-data/

    Model #1 has following architecture:
    Conv -> ReLU -> Pooling -> Affine -> ReLU -> Affine -> Softmax

    Architecture for Model 1

    Initial Parameters Description
    Weights Initialization HE Normal
    Weights Update Policy Adam
    Activation Function ReLU
    Regularization L2
    Pooling Max
    Loss Function Cross-entropy, Softmax


    Hyperparameters Description
    Filters for ConvNet Layer 32
    Size of the Filters height=width=7
    Params for ConvNet Layer stride=1, pad=3
    Params for Pooling Layer stride=2, height=width=2
    Dimension of Hidden Affine Layer 500 neurons
    Regularization 1e-3


    Training process of Model #1 with 18 000 iterations is shown on the figure below:



    Initialized Filters and Trained Filters for ConvNet Layer is shown on the figure below:



    Traffic Sign Classification with Model #1 in Real Time.
    Left: original frame with detected Sign.
    Upper Right: cut frame with detected Sign.
    Lower Right: classified frame by ConvNet according to the detected Sign.