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 | 73.55 | 17.5 | - | 3X GeForce RTX 2080 Ti | 2 | cfgs_fcos_voc07_res50_v3.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 | - |
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