Awesome

FCOS: Fully Convolutional One-Stage Object Detection

Abstract

This is a tensorflow re-implementation of FCOS: Fully Convolutional One-Stage Object Detection, and completed by YangXue.

Performance

Model	Backbone	Training data	Val data	mAP	Inf time (fps)	Model Link	Train Schedule	GPU	Image/GPU	Configuration File
Faster-RCNN	ResNet50_v1 600	VOC07 trainval	VOC07 test	73.09	-	-	-	1X GTX 1080Ti	1	-
FPN	ResNet50_v1 600	VOC07 trainval	VOC07 test	74.26	-	-	-	1X GTX 1080Ti	1	-
RetinaNet	ResNet50_v1d 600	VOC07 trainval	VOC07 test	74.00	14.6	model	-	4X GeForce RTX 2080 Ti	2	-
FCOS	ResNet50_v1d 896	VOC07 trainval	VOC07 test	72.25	14.3	Baidu Drive (ujvj)	-	3X GeForce RTX 2080 Ti	2	cfgs_fcos_voc07_res50_v4.py
RetinaNet	ResNet50_v1d 600	COCO train2017	COCO val2017 (coco minival)	34.3 (paper: 34.0)	12.2	model	1x	4X GeForce RTX 2080 Ti	2	-
FCOS	ResNet50_v1d 600	COCO train2017	COCO val2017 (coco minival)	34.8	12.2	Baidu Drive (qg62)	1x	3X GeForce RTX 2080 Ti	2	cfgs_fcos_coco_res50_1x_v1.py

My Development Environment

1、python3.5 (anaconda recommend)
2、cuda10.0
3、opencv(cv2)
4、tfplot (optional)
5、tensorflow >= 1.12

Download Model

Pretrain weights

1、Please download resnet50_v1, resnet101_v1 pre-trained models on Imagenet, put it to data/pretrained_weights.
2、(Recommend in this repo) Or you can choose to use a better backbone, refer to gluon2TF.

Baidu Drive, password: 5ht9.
Google Drive

Others

1、COCO dataset related

Compile

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

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

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 $PATH_ROOT/data/io/read_tfrecord.py

2、Make tfrecord

cd $PATH_ROOT/data/io/  
python convert_data_to_tfrecord_coco.py --VOC_dir='/PATH/TO/JSON/FILE/' 
                                        --save_name='train' 
                                        --dataset='coco'

3、Multi-gpu train

cd $PATH_ROOT/tools
multi_gpu_train.py

Eval

COCO

cd $PATH_ROOT/tools
python eval_coco.py --eval_data='/PATH/TO/IMAGES/'  
                    --eval_gt='/PATH/TO/TEST/ANNOTATION/'
                    --gpus='0,1,2,3,4,5,6,7'

PASCAL VOC

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

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
7、https://github.com/tianzhi0549/FCOS