Tradational nd Dilated CNN

we use Architecture used in the upper Paper in different dataset to explain the different models acheive a different accuracy

Dataset

about Traffic Sign Classification and Recognition

Link: https://www.kaggle.com/datasets/wjybuqi/traffic-sign-classification-and-recognition

This data set contains 6358 manually labeled category labels. The labels include the following 10 categories: “GuideSign” , “M1”, “M4, “M5”, “M6”, “M7”, “P1”, “P10_50”, “P12”, “W1”, corresponding to ten Different traffic sign categories .The data set contains one folders include 6358 images, and in the model separate it into training, validation, and testing. Traffic light classification is the process of automatically identifying traffic lights along a road, including speed limit signs (label in dataset P10_50), start signs (label in dataset m1), merging signs (label M4), and signs for people walking (label in dataset m7), no-parking signs(label in dataset p1),etc. The ability to Automatically recognize traffic lights .

implementation

Ratio used for training : 4062

Ratio used for Validation : 1270

Ratio used for testing : 1016

hyperparameters used in your model :

                                                      Adam (Learning rate =0.001)
                                                      Droupout (0.25)
                                                      Epochs :35
                                                      BatchSize=32
                                                      Adding additional hidden layer
                                                      Activation =relu
                                                      Use_Validation .20 of dataset
                                                      Increasing # of units in hidden layer to 128