Feature Pyramid Networks for Object Detection (Development Version)

Abstract

This repo is based on FPN, and completed by YangXue and YangJirui.

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

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
FPN_Res101_20181201(v1)	77.16	79.02	87.21	81.83	65.20	84.55	76.45	81.41	87.34	87.38	81.96	77.54	74.11	79.76	87.13	51.44	74.91	58.51	80.22	66.36	80.92
FPN_Res101_20181229(v2)	77.56	78.03	84.40	84.44	65.24	85.62	76.91	84.62	87.67	87.27	82.15	79.59	72.91	81.16	87.49	50.60	77.61	57.85	78.22	67.90	81.48
FPN_Res101_20181230(v3=v1+v2)	78.67	77.47	88.57	82.95	68.90	85.67	76.70	85.47	88.62	88.52	83.93	80.09	74.63	80.20	87.87	53.23	78.16	60.40	79.79	68.19	84.05
FPN_Res101_20190109(v4=v2-based)	78.36	76.59	88.42	81.42	69.53	84.15	78	85.92	87.25	88.14	82.61	81.65	72.11	88.38	87.35	50.14	76.91	56.95	80.07	66.39	85.18
FPN_Res101_20190110(v5=v1+v2+v4)	79.23	79.74	87.72	86.45	67.65	84.57	78.71	86.62	88.54	89.6	83.21	84.64	74.73	80.35	88.47	52.82	76.04	60.52	84.37	65.76	84.01

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

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_v1、resnet101_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

Tensorboard

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

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

0fine0/FPN_Tensorflow_DEV