Vehicle Logos Recognition

Overview

Classify 42 vehicle logos with different CNN models

dataset

We explore different models with Keras to do this job. Our dataset is about 25,000 and divided about 8:2 into training and validation dataset. Each image is resized to 64x64, so we change most models to fit our data which is originally designed for 224x224 image size.

models

• cifar10 A simple CNN model with just 4 Conv layers and relative few filters of kernel, achieves about 96% accuracy in val.
• AlexNet Classic AlexNet model with smaller first-layer kernel size and stride, achieves about 97.4% accuracy in val.
• VggNet Classic VggNet16 model without last block, achieves state-of-the-art about 98.5% accuracy in val.
• ResNet Classic ResNet model of new improved version, but performs not very well in this task with only 92% accuracy in val. We are trying to find results.

All the above networks are equiped with Batch Normalization, which is a effective method that helps the neural network converge quickly and achieve relative good results in just few epochs.

The original articles

• AlexNet ImageNet Classification with Deep Convolutional Neural Networks
• VggNet Very Deep Convolutional Networks for Large-Scale Image Recognition
• ResNet Deep Residual Learning for Image Recognition