By Songtao Liu, Di Huang, Yunhong Wang
Inspired by the structure of Receptive Fields (RFs) in human visual systems, we propose a novel RF Block (RFB) module, which takes the relationship between the size and eccentricity of RFs into account, to enhance the discriminability and robustness of features. We further assemble the RFB module to the top of SSD with a lightweight CNN model, constructing the RFB Net detector. You can use the code to train/evaluate the RFB Net for object detection. For more details, please refer to our ECCV paper.
System | mAP | FPS (Titan X Maxwell) |
---|---|---|
Faster R-CNN (VGG16) | 73.2 | 7 |
YOLOv2 (Darknet-19) | 78.6 | 40 |
R-FCN (ResNet-101) | 80.5 | 9 |
SSD300* (VGG16) | 77.2 | 46 |
SSD512* (VGG16) | 79.8 | 19 |
RFBNet300 (VGG16) | 80.7 | 83 |
RFBNet512 (VGG16) | 82.2 | 38 |
System | test-dev mAP | Time (Titan X Maxwell) |
---|---|---|
Faster R-CNN++ (ResNet-101) | 34.9 | 3.36s |
YOLOv2 (Darknet-19) | 21.6 | 25ms |
SSD300* (VGG16) | 25.1 | 22ms |
SSD512* (VGG16) | 28.8 | 53ms |
RetinaNet500 (ResNet-101-FPN) | 34.4 | 90ms |
RFBNet300 (VGG16) | 30.3 | 15ms |
RFBNet512 (VGG16) | 33.8 | 30ms |
RFBNet512-E (VGG16) | 34.4 | 33ms |
System | COCO minival mAP | #parameters |
---|---|---|
SSD MobileNet | 19.3 | 6.8M |
RFB MobileNet | 20.7 | 7.4M |
Please cite our paper in your publications if it helps your research:
@InProceedings{Liu_2018_ECCV,
author = {Liu, Songtao and Huang, Di and Wang, andYunhong},
title = {Receptive Field Block Net for Accurate and Fast Object Detection},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}
./make.sh
Note: Check you GPU architecture support in utils/build.py, line 131. Default is:
'nvcc': ['-arch=sm_52',
conda install opencv
To make things easy, we provide simple VOC and COCO dataset loader that inherits torch.utils.data.Dataset
making it fully compatible with the torchvision.datasets
API.
# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2007.sh # <directory>
# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2012.sh # <directory>
Install the MS COCO dataset at /path/to/coco from official website, default is ~/data/COCO. Following the instructions to prepare minival2014 and valminusminival2014 annotations. All label files (.json) should be under the COCO/annotations/ folder. It should have this basic structure
$COCO/
$COCO/cache/
$COCO/annotations/
$COCO/images/
$COCO/images/test2015/
$COCO/images/train2014/
$COCO/images/val2014/
UPDATE: The current COCO dataset has released new train2017 and val2017 sets which are just new splits of the same image sets.
First download the fc-reduced VGG-16 PyTorch base network weights at: https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth or from our BaiduYun Driver
MobileNet pre-trained basenet is ported from MobileNet-Caffe, which achieves slightly better accuracy rates than the original one reported in the paper, weight file is available at: https://drive.google.com/open?id=13aZSApybBDjzfGIdqN1INBlPsddxCK14 or BaiduYun Driver.
By default, we assume you have downloaded the file in the RFBNet/weights
dir:
mkdir weights
cd weights
wget https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
To train RFBNet using the train script simply specify the parameters listed in train_RFB.py
as a flag or manually change them.
python train_RFB.py -d VOC -v RFB_vgg -s 300
Note:
train_RFB.py
for options)To evaluate a trained network:
python test_RFB.py -d VOC -v RFB_vgg -s 300 --trained_model /path/to/model/weights
By default, it will directly output the mAP results on VOC2007 test or COCO minival2014. For VOC2012 test and COCO test-dev results, you can manually change the datasets in the test_RFB.py
file, then save the detection results and submitted to the server.