DetectionTeamUCAS / Faster Rcnn_tensorflow
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Faster-RCNN_Tensorflow
Abstract
This is a tensorflow re-implementation of Faster R-CNN: Towards Real-Time ObjectDetection with Region Proposal Networks.
This project is completed by YangXue and YangJirui. Some relevant projects (R2CNN) and (RRPN) based on this code.
Train on VOC 2007 trainval and test on VOC 2007 test (PS. This project also support coco training.)
Comparison
use_voc2012_metric
Models | mAP | sheep | horse | bicycle | bottle | cow | sofa | bus | dog | cat | person | train | diningtable | aeroplane | car | pottedplant | tvmonitor | chair | bird | boat | motorbike |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
resnet50_v1 | 75.16 | 74.08 | 89.27 | 80.27 | 55.74 | 83.38 | 69.35 | 85.13 | 88.80 | 91.42 | 81.17 | 81.71 | 62.74 | 78.65 | 86.86 | 47.00 | 76.71 | 50.29 | 79.05 | 60.51 | 80.96 |
resnet101_v1 | 77.03 | 79.68 | 89.33 | 83.89 | 59.41 | 85.68 | 76.59 | 84.23 | 88.50 | 88.50 | 81.54 | 79.16 | 72.66 | 80.26 | 88.42 | 47.50 | 79.81 | 52.85 | 80.70 | 59.94 | 81.87 |
mobilenet_v2 | 50.36 | 46.68 | 70.45 | 67.43 | 25.69 | 53.60 | 46.26 | 58.95 | 37.62 | 43.97 | 67.67 | 61.35 | 52.14 | 56.54 | 75.02 | 24.47 | 49.89 | 27.76 | 38.04 | 38.20 | 65.46 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
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 |
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 |
Requirements
1、tensorflow >= 1.2
2、cuda8.0
3、python2.7 (anaconda2 recommend)
4、opencv(cv2)
Download Model
1、please download resnet50_v1、resnet101_v1 pre-trained models on Imagenet, put it to $PATH_ROOT/data/pretrained_weights.
2、please download mobilenet_v2 pre-trained model on Imagenet, put it to $PATH_ROOT/data/pretrained_weights/mobilenet.
3、please download trained model by this project, put it to $PATH_ROOT/output/trained_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