This is a Keras implementation of YOLOv3 (Tensorflow backend), for tackling Kaggle 2018 Google AI Open Images - Object Detection Track competition. I forked the code from qqwweee/keras-yolo3 and made necessary modification for it to work for the Open Images dataset.

1. Download the original pre-trained Darknet weights for YOLOv3, and convert it to Keras format.

   $ wget https://pjreddie.com/media/files/darknet53.conv.74 -O darknet53.weights
   $ python3 convert.py -w darknet53.cfg darknet53.weights model_data/darknet53_weights.h5

2. Prepare the Open Images dataset for training. Reference: (a) the Kaggle competition page, (b) Overview of the Open Images Challenge 2018

   After downloading all necessary files, put the dataset into a directory structure like the following. Note that all jpg images for training (and validation) should be located in the train/ folder. The annotation files such as challenge-2018-class-descriptions-500.csv and challenge-2018-train-annotations-bbox.csv should be found in the kaggle-2018-object-detection folder. And the test jpg images should be in the kaggle-2018-object-detection/test_challenge_2018 folder.

   $ tree -d ~/data/open-images-dataset
   /home/user/data/open-images-dataset
   ├── kaggle-2018-object-detection
   │   └── test_challenge_2018
   ├── kaggle-2018-visual-relationships
   ├── test
   ├── train
   └── valid

   Then make a symbolic link in the project folder to the dataset directory. For example,

   $ cd ~/project/keras-yolo3
   $ ln -s ~/data/open-images-dataset .

1. Convert Open Images annotations into YOLOv3 format. Here is how YOLOv3 annotations look like:

   One row for one image; Row format: image_file_path box1 box2 ... boxN; Box format: x_min,y_min,x_max,y_max,class_id (no space). Here is an example:

   path/to/img1.jpg 50,100,150,200,0 30,50,200,120,3
   path/to/img2.jpg 120,300,250,600,2
   ...
   

   $ python3 openimgs_annotation.py

2. Do stage_1 and stage_2 training, with Adam optimizer. The darknet53.weights is used to load pre-trained weights. And the trained model weights would be saved as logs/001/trained_weights_stage_2.h5.

   $ python3 train1.py

3. (Optional) Use Learning Rate Finder (LRFinder) to find optimal learning rate range for the YOLOv3 model with the dataset. The result would be saved as lr_finder_loss.png. You can then fine-tune the learning rate settings in train2.py based on it.

   $ python3 train_lr_find.py

4. Do the final stage of training, with SGD and "Triangular3" Cyclic Learning Rate (CLR).

   $ python3 train2.py

Execute yolo_test.py in 'display' mode. By default, the script would use logs/002/trained_weights_final.h5 as the trained weight for testing.

$ python3 yolo_test.py --help
Using TensorFlow backend.
usage: yolo_test.py [-h] [--model MODEL] [--score SCORE] [--shuffle]
                    [--display] [--submit]

optional arguments:
  -h, --help     show this help message and exit
  --model MODEL  path to model weight file, default
                 logs/002/trained_weights_final.h5
  --score SCORE  score (confidence) threshold, default 0.001
  --shuffle      shuffle images for display mode
  --display      display mode, to show inferred images with bounding box
                 overlays
  --submit       submit mode, to generate "output.csv" for Kaggle submission

For example,

$ python3 yolo_test.py --display --score 0.3 --shuffle

Execute yolo_test.py in 'submit' mode. For example,

$ python3 yolo_test.py --submit

Then use filter_submit.py to set a different score (confidence) threshold for submission. Note that Kaggle might have problem processing the submission (csv) file if the file contains too many bounding boxes. We can effectively decrease the number of bounding boxes in the submission file by setting a higher score threshold.

$ python3 filter_submit.py --score 0.01 submit/output.csv submit/output-filtered.csv

1. The test environment is

   • Python 3.5.2
   • Keras 2.2.2
   • TensorFlow 1.9.0

2. Default anchors are used. If you use your own anchors, probably some changes are needed.

3. The tiny_yolo3 and train_bottlenet related code was removed, since I do not use them.