bmei1314/retinanet_tensorflow

Focal Loss for Dense Object Detection.

Focal Loss for Dense Object Detection

Abstract

This is a tensorflow re-implementation of Focal Loss for Dense Object Detection, and it is completed by YangXue.

Performance

Model	Backbone	Training data	Val data	mAP	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_v1 600	VOC07 trainval	VOC07 test	73.16	-	8X GeForce RTX 2080 Ti	1	cfgs_res50_voc07_v3.py
RetinaNet	ResNet50_v1d 600	VOC07 trainval	VOC07 test	73.26	-	8X GeForce RTX 2080 Ti	1	cfgs_res50_voc07_v4.py
RetinaNet	ResNet50_v1d 600	VOC07+12 trainval	VOC07 test	79.66	-	8X GeForce RTX 2080 Ti	1	cfgs_res50_voc0712_v1.py
RetinaNet	ResNet101_v1d 600	VOC07+12 trainval	VOC07 test	81.69	-	8X GeForce RTX 2080 Ti	1	cfgs_res50_voc0712_v4.py
RetinaNet	ResNet101_v1d 800	VOC07+12 trainval	VOC07 test	80.69	-	8X GeForce RTX 2080 Ti	1	cfgs_res50_voc0712_v3.py
RetinaNet	ResNet50_v1 600	COCO train2017	COCO val2017 (coco minival)	33.4	1x	8X GeForce RTX 2080 Ti	1	cfgs_res50_coco_1x_v4.py

My Development Environment

1、python3.5 (anaconda recommend)
2、cuda9.0
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、Or you can choose to use a better backbone, refer to gluon2TF. Pretrain Model Link, password: 5ht9.

Trained weights

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

Compile

cd $PATH_ROOT/libs/box_utils/cython_utils
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 line 76 of $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
python multi_gpu_train.py

Eval

cd $PATH_ROOT/tools
python eval_coco.py --eval_data='/PATH/TO/IMAGES/'  
                    --eval_gt='/PATH/TO/TEST/ANNOTATION/'
                    --GPU='0'
                    

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

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/fizyr/keras-retinanet