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A brand logo recognition system using deep convolutional neural networks.

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DeepLogo

A brand logo detection system using region-based convolutional neural networks.

Overview

Implentation of Region-based Convolutional Neural Networks in Tensorflow, to detect and classify brand logos.

Note: Since this is my first version model, there are some remaining issues.

Example detection results are below.

example1 example2 example3 example4 example5 example6

Here are some failure cases.

example7

Usage

  1. python gen_bg_class.py: Generate train_annot_with_bg_class.txt file.
  2. python crop_and_aug.py: Crop brand logo images from the flickr27_logos_dataset and apply data augmentation method. Finally the dataset consists of 140137 images.
  3. python gen_train_valid_test.py: Generate(Split) train/valid/test set from the dataset.
  4. python train_deep_logo_cnn.py: Train the convolutional neural networks and save the trained model to disk.
  5. python test_deep_logo_cnn.py: Test the trained model (for Classification).
    python detect_logo.py: Test the trained model (for Detection)

Network

The network is based on this blog post. Same network is applied to this brand logo recognition task because a brand logo is similar to a number plate which consists of a number of digits and letters.

License

MIT

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  • Python 100.0%