CarL-CNN

Car Logos CNN - building my own car logos classifying neural network

Description

CarL-CNN was trained with 20,778 50x50px RGB images depicting logotypes of 40 different car brands. The dataset was handpicked from loosely scraped websites and contains images, photos, drawings, sketches at various color schemes (black/white, RGB, CMYK, one-color), different angles and can sometimes contain some noise (other logotypes, background, etc.)

It classifies a given image by attributing a predicted brand label:

Model Metrics

The model got the below scores:

• Precision: 94.20%
• Recall: 94.03%
• F1 score: 94.04%
• Accuracy: 94.03%

Features

The Jupyter Notebook file contains method definitions for showcasing, class probability prediction and new image recognition. Also, a closer examination of the wrongly predicted cases is done to analyze which car brands need some dataset enrichment.

Future development

I plan to make it into a web application and enable adding user-owned images for classification. Later on, using sl4a, I plan to make it into an Android application and enable car logo recognition by mobile camera -- a True computer vision!

Links - needed for the full run of CarL-CNN

(unzip to folder, where CarLogosCNN.ipynb resides)

• Model weights (1 file, 140 MB) - http://u.42.pl/GEt0_model_weights
• Images (20778 files, 54 MB - ZIP) for the train and test set - http://u.42.pl/GEt7_images
• New images (12 files, 677 kB) unseen during the training - http://u.42.pl/GEtf_new_images

Requirements

• numpy
• matplotlib
• scikit-learn
• keras (tensorflow backend)
• pillow - for image processing