SyNet

Object Detection with Ensemble Networks

Summary

A combination method, SyNet, is proposed in this work which combines multi-stage detectors with single-stage ones with the motivation of decreasing the high false negative rate of multi-stage detectors and increasing the quality of the single-stage detector proposals. As building blocks, CenterNet and Cascade R-CNN with pretrained feature extractors are utilized along with an ensembling method.

Dependencies

These must be installed before next steps.

Python 3.5+
Tensorflow >= 2.0
Tensorpack 0.10
Torch 1.1.0
Torchvision 0.3.0

Training

3.1. Include the Dataset

First of all, transfer your dataset in MS-COCO format to tensorpack/examples/FasterRCNN/DATA/ and CenterNet/data/DATA/. Format must be same as:

DATA
├─  annotations
│   ├─  instances_train2017.json
│   └─  instances_val2017.json
├─  train2017
│   ├─  0.jpg
│   ├─  1.jpg
│   └─  ...
└─  val2017
    ├─  0.jpg
    ├─  5.jpg
    └─  ...

3.2. Training Cascade R-CNN

From http://models.tensorpack.com/#FasterRCNN, download ImageNet-R101-AlignPadding.npz under Faster R-CNN to tendorpack/examples/FasterRCNN/back/ folder. Then, start training by

python train.py --config BACKBONE.WEIGHTS= /path/to/tensorpack/examples/FasterRCNN/back/ImageNet-R101-AlignPadding.npz DATA.BASEDIR=/path/to/tensorpack/examples/FasterRCNN/DATA FPN.CASCADE=True FPN.NORM=GN BACKBONE.NORM=GN FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head BACKBONE.RESNET_NUM_BLOCKS=[3,4,23,3] TEST.RESULT_SCORE_THRESH=1e-4 PREPROC.TRAIN_SHORT_EDGE_SIZE=[640,800] TRAIN.LR_SCHEDULE=9x TRAIN.NUM_GPUS=1 MODE_MASK=False

Different training configurations can be found at https://github.com/tensorpack/tensorpack

3.3. Training CenterNet

Change the CenterNet/src/lib/datasets after adding your dataset describer to CenterNet/src/lib/datasets/custom_data.py which should be similar CenterNet/src/lib/datasets/dataset/coco.py. Furthermore, modify CenterNet/src/lib/utils/debugger.py and change class names to class names list of yout own dataset. Then, start training by

python /path/to/CenterNet/src/main.py ctdet --exp_id coco_dla_2x --dataset custom_data --batch_size 8 --master_batch 32 --lr 5e-4 --gpus 0 --num_workers 0

Different training configurations can be found at https://github.com/xingyizhou/CenterNet.

Results

4.1. COCO Dataset

Results for COCO val-2017 set are presented below with results of the state-of-the-art-methods.

Method	Feature Extractor	mAP (0.05:0.95)	mAP (0.50)	mAP (0.75)
SyNet (ours)	ResNet101 + DLA-34	47.2	66.4	52.1
Cascade R-CNN	ResNet101	42.7	61.6	46.6
Cascade R-CNN	ResNet50	40.3	59.4	43.7
CenterNet	ResNet50	40.3	59.1	44.0
CenterNet	ResNet50	37.4	55.1	40.8
Faster R-CNN	ResNet50	38.5	60.3	41.6
Faster R-CNN	ResNet50	36.4	58.4	39.1
Mask R-CNN	ResNet50	39.4	60.9	43.3
Mask R-CNN	ResNet50	37.3	59.0	40.2
Retina Net	ResNet50	37.7	57.5	40.4
Retina Net	ResNet50	35.6	55.5	38.3
Cascade Mask R-CNN	ResNet50	42.6	60.7	46.7
Cascade Mask R-CNN	ResNet50	41.2	59.1	45.1
Hybrid Task Cascade	ResNet50	44.9	63.8	48.7
Hybrid Task Cascade	ResNet50	43.2	62.1	46.8
EfficientDet-D7 (1536)	ResNet50	52.1	-	-

4.1. VisDrone Dataset

Results for VisDrone test-set are presented below with results of the state-of-the-art-methods.

Method	mAP (0.05:0.95)	mAP (0.50)	mAP (0.75)
SyNet (ours)	25.1	48.4	26.2
Cascade R-CNN	24.7	43.7	24.3
CenterNet	14.3	26.6	13.1

Acknowledgement

For the training of the Cascade R-CNN, Tensorpack is used: https://github.com/tensorpack/tensorpack For the training of the CenterNet, CenterNet is used: https://github.com/xingyizhou/CenterNet For the weighted box ensemble, https://github.com/ZFTurbo/Weighted-Boxes-Fusion is used.

mertalbaba / SyNet

readme