A development version based on FPN.
Support multi-gpu training!
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.
| 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
| 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 |
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
Please download resnet50_v1、resnet101_v1 pre-trained models on Imagenet, put it to $PATH_ROOT/data/pretrained_weights.
├── VOCdevkit
│ ├── VOCdevkit_train
│ ├── Annotation
│ ├── JPEGImages
│ ├── VOCdevkit_test
│ ├── Annotation
│ ├── JPEGImages
cd $PATH_ROOT/libs/box_utils/cython_utils
python setup.py build_ext --inplace
Select a configuration file in the folder ($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'
cd $PATH_ROOT/tools
python eval.py --eval_imgs='/PATH/TO/IMAGES/'
--annotation_dir='/PATH/TO/TEST/ANNOTATION/'
--GPU='0'
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
cd $PATH_ROOT/output/summary
tensorboard --logdir=.
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