The Equalization Losses for Long-tailed Object Detection and Instance Segmentation

This repo is official implementation CVPR 2021 paper: Equalization Loss v2: A New Gradient Balance Approach for Long-tailed Object Detection and CVPR 2020 paper: Equalization loss for long-tailed object recognition

Besides the equalization losses, this repo also includes some other algorithms:

BAGS (Balance GroupSoftmax)
cRT (classifier re-training)
LWS (Learnable Weight Scaling)

Requirements

python 3.6+
pytorch 1.5.0
torchvision 0.6.0
mmdet 2.3
mmcv 1.0.5

We test our codes on MMDetection V2.3, other versions should also be ok.

Prepare LVIS Dataset

for images

LVIS uses same images as COCO's, so you need to donwload COCO dataset at folder ($COCO), and link those train, val under folder lvis($LVIS).

mkdir -p data/lvis
ln -s $COCO/train $LVIS
ln -s $COCO/val $LVIS
ln -s $COCO/test $LVIS

for annotations

Download the annotations from lvis webset

cd $LVIS
mkdir annotations

then places the annotations at folder ($LVIS/annotations)

Finally you will have the file structure like below:

data
  ├── lvis
  |   ├── annotations
  │   │   │   ├── lvis_v1_val.json
  │   │   │   ├── lvis_v1_train.json
  │   ├── train2017
  │   │   ├── 000000004134.png
  │   │   ├── 000000031817.png
  │   │   ├── ......
  │   ├── val2017
  │   ├── test2017

for API

The official lvis-api and mmlvis can lead to some bugs of multiprocess. See issue

So you can install this LVIS API from my modified repo.

pip install git+https://github.com/tztztztztz/lvis-api.git

Testing with pretrain_models

# ./tools/dist_test.sh ${CONFIG} ${CHECKPOINT} ${GPU_NUM} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]
./tools/dist_test.sh configs/eqlv2/eql_r50_8x2_1x.py data/pretrain_models/eql_r50_8x2_1x.pth 8 --out results.pkl --eval bbox segm

Training

# ./tools/dist_train.sh ${CONFIG} ${GPU_NUM}
./tools/dist_train.sh ./configs/end2end/eql_r50_8x2_1x.py 8

Once you finished the training, you will get the evaluation metric like this:

bbox AP

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=all] = 0.242
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=300 catIds=all] = 0.401
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=300 catIds=all] = 0.254
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=     s | maxDets=300 catIds=all] = 0.181
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=     m | maxDets=300 catIds=all] = 0.317
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=     l | maxDets=300 catIds=all] = 0.367
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=  r] = 0.135
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=  c] = 0.225
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=  f] = 0.308
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=all] = 0.331
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=     s | maxDets=300 catIds=all] = 0.223
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=     m | maxDets=300 catIds=all] = 0.417
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=     l | maxDets=300 catIds=all] = 0.497
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=  r] = 0.197
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=  c] = 0.308
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=  f] = 0.415

mask AP

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=all] = 0.237
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=300 catIds=all] = 0.372
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=300 catIds=all] = 0.251
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=     s | maxDets=300 catIds=all] = 0.169
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=     m | maxDets=300 catIds=all] = 0.316
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=     l | maxDets=300 catIds=all] = 0.370
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=  r] = 0.149
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=  c] = 0.228
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=  f] = 0.286
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=all] = 0.326
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=     s | maxDets=300 catIds=all] = 0.210
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=     m | maxDets=300 catIds=all] = 0.415
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=     l | maxDets=300 catIds=all] = 0.495
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=  r] = 0.213
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=  c] = 0.313
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=  f] = 0.389

We place ours configs file in ./configs/

./configs/end2end: eqlv2 and other end2end methods
./configs/decouple decoupled-based methods

How to train decouple training methods.

Train the baseline model (or EQL v2).
Prepare the pretrained checkpoint

  # suppose you've trained baseline model
  cd r50_1x
  python ../tools/ckpt_surgery.py --ckpt-path epoch_12.pth --method remove
  # if you want to train LWS, you should choose method 'reset'

Start training with configs

  # ./tools/dist_train.sh ./configs/decouple/bags_r50_8x2_1x.py 8
  # ./tools/dist_train.sh ./configs/decouple/lws_r50_8x2_1x.py 8
  ./tools/dist_train.sh ./configs/decouple/crt_r50_8x2_1x.py 8

Pretrained Models on LVIS

Methods	end2end	AP	APr	APc	APf	pretrained_model
Baseline	√	16.1	0.0	12.0	27.4	model
EQL	√	18.6	2.1	17.4	27.2	model
RFS	√	22.2	11.5	21.2	28.0	model
LWS	×	17.0	2.0	13.5	27.4	model
cRT	×	22.1	11.9	20.2	29.0	model
BAGS	×	23.1	13.1	22.5	28.2	model
EQLv2	√	23.7	14.9	22.8	28.6	model

How to train EQLv2 on OpenImages

1. Download the data

Download openimages v5 images from link, The folder will be

openimages
    ├── train
    ├── validation
    ├── test

Download the annotations for Challenge 2019 from link, The folder will be

annotations
    ├── challenge-2019-classes-description-500.csv
    ├── challenge-2019-train-detection-human-imagelabels.csv
    ├── challenge-2019-train-detection-bbox.csv
    ├── challenge-2019-validation-detection-bbox.csv
    ├── challenge-2019-validation-detection-human-imagelabels.csv
    ├── ...

2. Convert the `.csv` to coco-like `.json` file.

cd tools/openimages2coco/
python convert_annotations.py -p PATH_TO_OPENIMAGES --version challenge_2019 --task bbox

You may need to donwload the data directory from https://github.com/bethgelab/openimages2coco/tree/master/data and place it at $project_dir/tools/openimages2coco/

3. Train models

  ./tools/dist_train.sh ./configs/openimages/eqlv2_r50_fpn_8x2_2x.py 8

Other configs can be found at ./configs/openimages/

4. Inference and output the json results file

./tools/dist_test.sh ./configs/openimages/eqlv2_r50_fpn_8x2_2x.py openimage_eqlv2_2x/epoch_1.pth 8 --format-only --options "jsonfile_prefix=openimage_eqlv2_2x/results""

Then you will get results.bbox.json under folder openimage_eqlv2

5. Convert `coco-like json` result file to `openimage-like csv` results file

cd $project_dir/tools/openimages2coco/
python convert_predictions.py -p ../../openimage_eqlv2/results.bbox.json --subset validation

Then you will get results.bbox.csv under folder openimage_eqlv2

6. Evaluate results file using official API

Please refer this link

After this, you will see something like this.

OpenImagesDetectionChallenge_Precision/[email protected],0.5263230244227198                                                                                                                     OpenImagesDetectionChallenge_PerformanceByCategory/[email protected]/b'/m/061hd_',0.4198356678732905                                                                                             OpenImagesDetectionChallenge_PerformanceByCategory/[email protected]/b'/m/06m11',0.40262261023434986                                                                                             OpenImagesDetectionChallenge_PerformanceByCategory/[email protected]/b'/m/03120',0.5694096972722996                                                                                              OpenImagesDetectionChallenge_PerformanceByCategory/[email protected]/b'/m/01kb5b',0.20532245532245533                                                                                            OpenImagesDetectionChallenge_PerformanceByCategory/[email protected]/b'/m/0120dh',0.7934685035604202                                                                                             OpenImagesDetectionChallenge_PerformanceByCategory/[email protected]/b'/m/0dv5r',0.7029194449221794                                                                                              OpenImagesDetectionChallenge_PerformanceByCategory/[email protected]/b'/m/0jbk',0.5959245714028935

7. Parse the AP file and output the grouped AP

cd $project_dir

PYTHONPATH=./:$PYTHONPATH python tools/parse_openimage_metric.py --file openimage_eqlv2_2x/metric

And you will get:

mAP 0.5263230244227198
mAP0: 0.4857693606436219
mAP1: 0.52047262478471
mAP2: 0.5304580597832517
mAP3: 0.5348747991854581
mAP4: 0.5588236678031849

Main Results on OpenImages

Methods	AP	AP1	AP2	AP3	AP4	AP5
Faster-R50	43.1	26.3	42.5	45.2	48.2	52.6
EQL	45.3	32.7	44.6	47.3	48.3	53.1
EQLv2	52.6	48.6	52.0	53.0	53.4	55.8
Faster-R101	46.0	29.2	45.5	49.3	50.9	54.7
EQL	48.0	36.1	47.2	50.5	51.0	55.0
EQLv2	55.1	51.0	55.2	56.6	55.6	57.5

Citation

If you use the equalization losses, please cite our papers.

@article{tan2020eqlv2,
  title={Equalization Loss v2: A New Gradient Balance Approach for Long-tailed Object Detection},
  author={Tan, Jingru and Lu, Xin and Zhang, Gang and Yin, Changqing and Li, Quanquan},
  journal={arXiv preprint arXiv:2012.08548},
  year={2020}
}

@inproceedings{tan2020equalization,
  title={Equalization loss for long-tailed object recognition},
  author={Tan, Jingru and Wang, Changbao and Li, Buyu and Li, Quanquan and Ouyang, Wanli and Yin, Changqing and Yan, Junjie},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11662--11671},
  year={2020}
}

Credits

The code for converting openimage to LVIS is from this repo.

The official implementation of Equalization Loss v1 & v2 (CVPR 2020, 2021) based on MMDetection.

Related tags

Overview

The Equalization Losses for Long-tailed Object Detection and Instance Segmentation

Requirements

Prepare LVIS Dataset

Testing with pretrain_models

Training

How to train decouple training methods.

Pretrained Models on LVIS

How to train EQLv2 on OpenImages

1. Download the data

2. Convert the .csv to coco-like .json file.

3. Train models

4. Inference and output the json results file

5. Convert coco-like json result file to openimage-like csv results file

6. Evaluate results file using official API

7. Parse the AP file and output the grouped AP

Main Results on OpenImages

Citation

Credits

Owner

Jingru Tan

Demo code for paper "Learning optical flow from still images", CVPR 2021.

Distributed Asynchronous Hyperparameter Optimization in Python

Collection of NLP model explanations and accompanying analysis tools

Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis

PyTorch implementation of the Value Iteration Networks (VIN) (NIPS '16 best paper)

simple demo codes for Learning to Teach with Dynamic Loss Functions

XtremeDistil framework for distilling/compressing massive multilingual neural network models to tiny and efficient models for AI at scale

Python script that allows you to automatically setup your Growtopia server.

pytorch implementation of the ICCV'21 paper "MVTN: Multi-View Transformation Network for 3D Shape Recognition"

PyTorch Implementation of NCSOFT's FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis

Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on PaddlePaddle

Implementation of the HMAX model of vision in PyTorch

Joint learning of images and text via maximization of mutual information

DeepCO3: Deep Instance Co-segmentation by Co-peak Search and Co-saliency

Baselines for TrajNet++

Self-Supervised Monocular DepthEstimation with Internal Feature Fusion(arXiv), BMVC2021

Running AlphaFold2 (from ColabFold) in Azure Machine Learning

Scale-aware Automatic Augmentation for Object Detection (CVPR 2021)

A framework to train language models to learn invariant representations.

🏅 The Most Comprehensive List of Kaggle Solutions and Ideas 🏅

2. Convert the `.csv` to coco-like `.json` file.

5. Convert `coco-like json` result file to `openimage-like csv` results file