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
Online demo
http://nn-box.com/box/ - upload image wait several seconds and it will show boxes. ResNet152 is used as backbone.
Requirements
Python 3.5, Keras 2.2, Keras-RetinaNet 0.4.1
Pretrained models 2018
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, 768 - 1024, 533 MB, 178 MB, 0.4621, 0.3520, ResNet101, 768 - 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
Pretrained models 2019
There are 3 RetinaNet models based on ResNet50, ResNet101 and ResNet152 for all 500 classes., Backbone, Image Size (px), Model (training), Model (inference), Small validation mAP, LB (Public), ---, ---, ---, ---, ---, ---, ResNet50, 768 - 1024, 534 MB, 178 MB, 0.4594, 0.4223, ResNet101, 768 - 1024, 752 MB, 251 MB, 0.4986, 0.4520, ResNet152, 600 - 800, 932 MB, 312 MB, 0.4991, 0.4651, ## 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 Level 1 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:
Proposed method increases the overall performance:
- ResNet101 mAP 0.3776 + ResNet152 mAP 0.3840 gives in result: mAP 0.4220