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DeepLogo

A brand logo detection system using tensorflow object detection API.

Examples

Belows are detection examples.

example1 example2 example3 example4 example5 example6

Usage

  1. Setup the tensorflow object detection API. First of all, clone the tensorflow/models repository.

    $ git clone https://github.com/tensorflow/models.git
$ cd models/research/object_detection
$ wget http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2018_01_28.tar.gz
$ tar zxvf ssd_inception_v2_coco_2018_01_28.tar.gz

    For detailed steps to setup, please follow the installation.

  2. Clone the DeepLogo repository.

    $ git clone https://github.com/satojkovic/DeepLogo.git

  3. Download dataset from flickr_27_logos_dataset and extract.

    $ cd DeepLogo
$ wget http://image.ntua.gr/iva/datasets/flickr_logos/flickr_logos_27_dataset.tar.gz
$ tar zxvf flickr_logos_27_dataset.tar.gz
$ cd flickr_logos_27_dataset
$ tar zxvf flickr_logos_27_dataset_images.tar.gz
$ cd ../

  4. Preprocess original annotation file and generate flickr_logos_27_dataset_training_set_annotation_cropped.txt and flickr_logos_27_dataset_test_set_annotation_cropped.txt. These two files are used to generate tfrecord files.

    $ cd DeepLogo
$ python preproc_annot.py

  5. Generate tfrecord files.

    $ python gen_tfrecord.py --train_or_test train --csv_input flickr_logos_27_dataset/flickr_logos_27_dataset_training_set_annotation_cropped.txt --img_dir flickr_logos_27_dataset/flickr_logos_27_dataset_images --output_path train.tfrecord
$ python gen_tfrecord.py --train_or_test test --csv_input flickr_logos_27_dataset/flickr_logos_27_dataset_test_set_annotation_cropped.txt --img_dir flickr_logos_27_dataset/flickr_logos_27_dataset_images --output_path test.tfrecord

  6. Training logo detector using pre-trained SSD.

    $ ln -s <OBJECT_DETECTION_API_DIR>/ssd_inception_v2_coco_2018_01_28 ssd_inception_v2_coco_2018_01_28
$ python <OBJECT_DETECTION_API_DIR>/legacy/train.py --logtostderr --pipeline_config_path=ssd_inception_v2.config --train_dir=training

    <OBJECT_DETECTION_API_DIR> is the absolute path of models/research/object_detection at step1.

  7. Export as pb file.

    $ python <OBJECT_DETECTION_API_DIR>/export_inference_graph.py --input_type=image_tensor --pipeline_config_path=ssd_inception_v2.config --trained_checkpoint_prefix=model.ckpt-<STEPS> --output_directory=logos_inference_graph

    <STEPS> is the steps at training, for example model.ckpt-1234.

  8. Testing logo detector.

    $ python logo_detection.py --model_name logos_inference_graph/ --label_map flickr_logos_27_label_map.pbtxt --test_annot_text flickr_logos_27_dataset/flickr_logos_27_dataset_test_set_annotation_cropped.txt --test_image_dir flickr_logos_27_dataset/flickr_logos_27_dataset_images --output_dir detect_results

Evaluation

First, modify num_examples field in training/pipeline.config file.

eval_config: {
  num_examples: 438
  # Note: The below line limits the evaluation process to 10 evaluations.
  # Remove the below line to evaluate indefinitely.
  max_evals: 10
}

This value is from flickr_logos_27_dataset_test_set_annotation_cropped.txt file.

$ wc -l flickr_logos_27_dataset/flickr_logos_27_dataset_test_set_annotation_cropped.txt 
     438 flickr_logos_27_dataset/flickr_logos_27_dataset_test_set_annotation_cropped.txt

Then start evaluation process by using eval.py provided within tensorflow/models repository.

$ python <OBJECT_DETECTION_API_DIR>/legacy/eval.py --logtostderr --checkpoint_dir=training --eval_dir=eval --pipeline_config_path=training/pipeline.config

After a while you will get evaluation results. If you want to check the results visually, open tensorboard in your browser.

$ tensorboard --logdir=eval/

License

MIT