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https://github.com/zfturbo/keras-retinanet-for-open-images-challenge-2018

Code for 15th place in Kaggle Google AI Open Images - Object Detection Track
https://github.com/zfturbo/keras-retinanet-for-open-images-challenge-2018

keras keras-retinanet object-detection pretrained-models

Last synced: 3 days ago
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Code for 15th place in Kaggle Google AI Open Images - Object Detection Track

Host: GitHub
URL: https://github.com/zfturbo/keras-retinanet-for-open-images-challenge-2018
Owner: ZFTurbo
License: mit
Created: 2018-08-31T14:40:24.000Z (over 6 years ago)
Default Branch: master
Last Pushed: 2023-03-24T22:24:50.000Z (almost 2 years ago)
Last Synced: 2025-01-22T09:07:02.954Z (11 days ago)
Topics: keras, keras-retinanet, object-detection, pretrained-models
Language: Python
Size: 58.6 KB
Stars: 268
Watchers: 13
Forks: 76
Open Issues: 17
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE

Awesome Lists containing this project

README

        ## 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 2.3.1, [Keras-RetinaNet 0.5.1](https://github.com/fizyr/keras-retinanet)

## Pretrained models 2018

There are 3 RetinaNet models based on ResNet50, ResNet101 and ResNet152 for [443 classes](https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/blob/master/a00_utils_and_constants.py#L36) (only Level 1). 

| Backbone | Image Size (px) | Model (training) | Model (inference) | Small validation mAP | Full validation mAP |

| --- | --- | --- | --- | --- |  --- |

| ResNet50 | 768 - 1024 | [533 MB](https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/releases/download/v1.1/retinanet_resnet50_level_1.h5) | [178 MB](https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/releases/download/v1.1/retinanet_resnet50_level_1_converted.h5) | 0.4621 | 0.3520 |

| ResNet101 | 768 - 1024 | [739 MB](https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/releases/download/v1.2/retinanet_resnet101_level_1_v1.2.h5) | [247 MB](https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/releases/download/v1.2/retinanet_resnet101_level_1_v1.2_converted.h5) | 0.5031 | 0.3870 |

| ResNet152 | 600 - 800 | [918 MB](https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/releases/download/v1.2/retinanet_resnet152_level_1_v1.2.h5) | [308 MB](https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/releases/download/v1.2/retinanet_resnet152_level_1_v1.2_converted.h5) | 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](https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/blob/master/a00_utils_and_constants.py#L130). 

| Backbone | Image Size (px) | Model (training) | Model (inference) | Small validation mAP | LB (Public) |

| --- | --- | --- | --- | --- |  --- |

| ResNet50 | 768 - 1024 | [534 MB](https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/releases/download/v1.3/retinanet_resnet50_500_classes_0.4594.h5) | [178 MB](https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/releases/download/v1.3/retinanet_resnet50_500_classes_0.4594_converted.h5) | 0.4594 | 0.4223 |

| ResNet101 | 768 - 1024 | [752 MB](https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/releases/download/v1.3/retinanet_resnet101_500_classes_0.4986.h5) | [251 MB](https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/releases/download/v1.3/retinanet_resnet101_500_classes_0.4986_converted.h5) | 0.4986 | 0.4520 |

| ResNet152 | 600 - 800 | [932 MB](https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/releases/download/v1.3/retinanet_resnet152_500_classes_0.4991.h5) | [312 MB](https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/releases/download/v1.3/retinanet_resnet152_500_classes_0.4991_converted.h5) | 0.4991 | 0.4651 |

## Inference 

Example can be found here: [retinanet_inference_example.py](https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/blob/master/retinanet_inference_example.py)

You need to change [files_to_process = glob.glob(DATASET_PATH + 'validation_big/\*.jpg')](https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/blob/master/retinanet_inference_example.py#L181) 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](https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/blob/master/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](https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/blob/master/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](https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/blob/master/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