Keras-RetinaNet for Open Images Challenge 2018

This code was used to get 15th place in Kaggle Google AI Open Images - Object Detection Track competition: https://www.kaggle.com/c/google-ai-open-images-object-detection-track/leaderboard

Repository contains the following:

• Pre-trained models (with ResNet101 and ResNet152 backbones)
• Example code to get predictions with these models for any set of images
• Code to train your own classifier based on Keras-RetinaNet and OID dataset
• Code to expand predictions for full 500 classes

Requirements

Python 3.5, Keras 2.2, Keras-RetinaNet 0.4.1

Pretrained models

There are 3 RetinaNet models based on ResNet50, ResNet101 and ResNet152 for 443 classes (only Level 1).

Backbone	Image Size (px)	Model (training)	Model (inference)	Small validation mAP	Full validation mAP
ResNet50	728 - 1024	533 MB	178 MB	0.4621	0.3520
ResNet101	728 - 1024	739 MB	247 MB	0.5031	0.3870
ResNet152	600 - 800	918 MB	308 MB	0.5194	0.3959

• Model (training) - can be used to resume training or can be used as pretrain for your own classifier
• Model (inference) - can be used to get prediction boxes for arbitrary images

Inference

Example can be found here: retinanet_inference_example.py

You need to change files_to_process = glob.glob(DATASET_PATH + 'validation_big/*.jpg') to your own set of files. On output you will get "predictions_*.csv" file with boxes.

Having these predictions you can expand it to all 500 classes using code from create_higher_level_predictions_from_level_1_predictions_csv.py

Training

For training you need to download OID dataset (~500 GB images): https://storage.googleapis.com/openimages/web/challenge.html

Next fix paths in a00_utils_and_constants.py

Then to train on OID dataset you need to run python files in following order:

• create_files_for_training_by_levels.py
• retinanet_training_level_1/find_image_parameters.py

then

• retinanet_training_level_1/train_oid_level_1_resnet101.py

or

• retinanet_training_level_1/train_oid_level_1_resnet152.py

Ensembles

If you have predictions from several models, for example for ResNet101 and ResNet152 backbones, then you can ensemble boxes with script:

• ensemble_predictions_with_weighted_method.py

Proposed method increases the overall performance:

• ResNet101 mAP 0.3776 + ResNet152 mAP 0.3840 gives in result: mAP 0.4220

Method description

• https://www.kaggle.com/c/google-ai-open-images-object-detection-track/discussion/64633