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Feature Pyramid Networks for Object Detection

Note

A development version based on FPN.
Support multi-gpu training!

Abstract

This is a tensorflow re-implementation of Feature Pyramid Networks for Object Detection.

This project is based on Faster-RCNN, and completed by YangXue and YangJirui.

Train on VOC 2007 trainval and test on VOC 2007 test (PS. This project also support coco training.)

1

Comparison

use_voc2007_metric

Models mAP sheep horse bicycle bottle cow sofa bus dog cat person train diningtable aeroplane car pottedplant tvmonitor chair bird boat motorbike
Faster-RCNN resnet50_v1 73.09 72.11 85.63 77.74 55.82 81.19 67.34 82.44 85.66 87.34 77.49 79.13 62.65 76.54 84.01 47.90 74.13 50.09 76.81 60.34 77.47
Faster-RCNN resnet101_v1 74.63 76.35 86.18 79.87 58.73 83.4 74.75 80.03 85.4 86.55 78.24 76.07 70.89 78.52 86.26 47.80 76.34 52.14 78.06 58.90 78.04
Faster-RCNN mobilenet_v2 50.34 46.99 68.45 65.89 28.16 53.21 46.96 57.80 38.60 44.12 66.20 60.49 52.40 56.06 72.68 26.91 49.99 30.18 39.38 38.54 64.74
FPN resnet50_v1 74.26 73.27 82.23 82.99 61.27 80.59 72.73 81.37 85.26 84.76 80.33 77.43 65.31 79.18 85.78 46.47 73.10 55.99 76.11 59.80 81.19
FPN resnet101_v1 76.14 74.63 85.13 81.67 63.79 82.43 77.83 83.07 86.45 85.82 81.08 81.01 71.22 80.01 86.30 48.05 73.89 56.99 78.33 62.91 82.24
FPN resnet101_v1+ 75.71 74.83 83.55 82.47 65.49 77.85 71.74 80.98 86.61 87.14 81.02 77.76 71.26 79.82 86.78 51.64 77.45 56.12 79.44 60.55 81.69
FPN resnet101_v1++ 75.89 76.05 84.22 80.29 63.21 83.04 78.69 81.81 86.61 85.61 79.75 79.78 71.27 80.33 86.24 49.03 76.81 56.32 78.51 60.37 79.91

+: SHARE_NET=False
++: SHORT_SIDE_LEN=800, FAST_RCNN_MINIBATCH_SIZE=512

COCO

Model Backbone Train Schedule GPU Image/GPU FP16 Box AP(Mask AP)
Faster (ours) R50v1-FPN 1X 1X TITAN Xp 1 no 36.1
Faster (ours) R50v1-FPN 1X 4X TITAN Xp 1 no 36.1
Faster (Face++ & Detectron) R50v1-FPN 1X 8X TITAN Xp 2 no 36.4
Faster (SimpleDet) R50v1-FPN 1X 8X 1080Ti 2 no 36.5

2

My Development Environment

1、python3.5 (anaconda recommend)
2、cuda9.0 (If you want to use cuda8, please set CUDA9 = False in the cfgs.py file.)
3、opencv(cv2)
4、tfplot
5、tensorflow == 1.10

Download Model

Please download resnet50_v1resnet101_v1 pre-trained models on Imagenet, put it to $PATH_ROOT/data/pretrained_weights.

Data Format

├── VOCdevkit
│   ├── VOCdevkit_train
│       ├── Annotation
│       ├── JPEGImages
│   ├── VOCdevkit_test
│       ├── Annotation
│       ├── JPEGImages

Compile

cd $PATH_ROOT/libs/box_utils/cython_utils
python setup.py build_ext --inplace

Demo(available)

Select a configuration file in the folder ($PATH_ROOT/libs/configs/) and copy its contents into cfgs.py, then download the corresponding weights.

cd $PATH_ROOT/tools
python inference.py --data_dir='/PATH/TO/IMAGES/' 
                    --save_dir='/PATH/TO/SAVE/RESULTS/' 
                    --GPU='0'

Eval

cd $PATH_ROOT/tools
python eval.py --eval_imgs='/PATH/TO/IMAGES/'  
               --annotation_dir='/PATH/TO/TEST/ANNOTATION/'
               --GPU='0'

Train

1、If you want to train your own data, please note:

(1) Modify parameters (such as CLASS_NUM, DATASET_NAME, VERSION, etc.) in $PATH_ROOT/libs/configs/cfgs.py
(2) Add category information in $PATH_ROOT/libs/label_name_dict/lable_dict.py     
(3) Add data_name to line 76 of $PATH_ROOT/data/io/read_tfrecord.py 

2、make tfrecord

cd $PATH_ROOT/data/io/  
python convert_data_to_tfrecord.py --VOC_dir='/PATH/TO/VOCdevkit/VOCdevkit_train/' 
                                   --xml_dir='Annotation'
                                   --image_dir='JPEGImages'
                                   --save_name='train' 
                                   --img_format='.jpg' 
                                   --dataset='pascal'

3、train

cd $PATH_ROOT/tools
python train.py

4、multi-gpu train

cd $PATH_ROOT/tools
python multi_gpu_train.py

Tensorboard

cd $PATH_ROOT/output/summary
tensorboard --logdir=.

3 4

Reference

1、https://github.com/endernewton/tf-faster-rcnn
2、https://github.com/zengarden/light_head_rcnn
3、https://github.com/tensorflow/models/tree/master/research/object_detection
4、https://github.com/CharlesShang/FastMaskRCNN
5、https://github.com/matterport/Mask_RCNN
6、https://github.com/msracver/Deformable-ConvNets

About

This is a tensorflow re-implementation of Feature Pyramid Networks for Object Detection.

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