DRFBNet300

By Seongkyun Han, Juwon Kwon, Jisang Yoo, Soonchul Kwon

• This repo is came from RFBNet and modified for my experiment.

• Seongkyun Han, Jisang Yoo, and Soonchul Kwon. METHOD AND APPARATUS FOR REALTIME OBJECT DETECTION IN UNMANNED AERIAL VEHICLE IMAGE. US Patent 16/711,894 (10-2019-0154978 (KR)), filed Dec 12 (Nov 28), 2019

Introduction

Installation

• Install PyTorch-1.0.1 by selecting your environment on the website and running the appropriate command.
• Clone this repository. This repository is mainly based on ssd.pytorch and Chainer-ssd, a huge thank to them.
  • Note: We currently only support PyTorch-0.4.0 and Python 3+.
• Compile the nms and coco tools:

./make.sh

Note: Check you GPU architecture support in utils/build.py, line 131. Default is:

'nvcc': ['-arch=sm_52',

• Then download the dataset by following the instructions below and install opencv.

conda install opencv

Note: For training, we currently support VOC and COCO.

Datasets

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.

VOC Dataset

Download VOC2007 trainval & test

# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2007.sh # <directory>

Download VOC2012 trainval

# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2012.sh # <directory>

COCO Dataset

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.

Training

• 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 DNet300/weights dir:

mkdir weights
cd weights
wget https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth

• To train DRFBNet using the train script simply specify the parameters listed in train.py as a flag or manually change them.
• See the train.py file for details

python train.py -d dataset_name -v version_name -s 300 

• Note:
  • -d: choose datasets, VOC or COCO, custom.
  • -v: choose backbone version, RFB_VGG, RFB_E_VGG or RFB_mobile.
  • -s: image size, 300 or 512.
  • You can pick-up training from a checkpoint by specifying the path as one of the training parameters (again, see train_RFB.py for options)
  • If you want to reproduce the results in the paper, the VOC model should be trained about 240 epoches while the COCO version need 130 epoches.

Evaluation

• To evaluate a trained network:
• See the test.py, val_custom.py file for details

python test.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.