FasterRCNN

Pytorch Implementation of FasterRCNN.
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Environment

OS: Ubuntu 16.04
Python: python3.x with torch==1.2.0, torchvision==0.4.0

Performance

Backbone	Train	Test	Pretrained Model	Epochs	Learning Rate	RoI per image	AP
ResNet-18	trainval35k	minival5k	Pytorch	12	2e-2/2e-3/2e-4	512	27.1
ResNet-34	trainval35k	minival5k	Pytorch	12	2e-2/2e-3/2e-4	512	33.5
ResNet-50	trainval35k	minival5k	Pytorch	12	2e-2/2e-3/2e-4	512	34.9
ResNet-101	trainval35k	minival5k	Pytorch	12	2e-2/2e-3/2e-4	512	38.6

Trained models

You could get the trained models reported above at 
https://drive.google.com/open?id=1JYs4r1M6doRlMgKCxSWmue2iKAcMkJxe

Usage

Setup

cd libs
sh make.sh

Train

usage: train.py [-h] --datasetname DATASETNAME --backbonename BACKBONENAME
                [--checkpointspath CHECKPOINTSPATH]
optional arguments:
  -h, --help            show this help message and exit
  --datasetname DATASETNAME
                        dataset for training.
  --backbonename BACKBONENAME
                        backbone network for training.
  --checkpointspath CHECKPOINTSPATH
                        checkpoints you want to use.
cmd example:
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python train.py --datasetname coco --backbonename resnet50

Test

usage: test.py [-h] --datasetname DATASETNAME [--annfilepath ANNFILEPATH]
               [--datasettype DATASETTYPE] --backbonename BACKBONENAME
               --checkpointspath CHECKPOINTSPATH [--nmsthresh NMSTHRESH]
optional arguments:
  -h, --help            show this help message and exit
  --datasetname DATASETNAME
                        dataset for testing.
  --annfilepath ANNFILEPATH
                        used to specify annfilepath.
  --datasettype DATASETTYPE
                        used to specify datasettype.
  --backbonename BACKBONENAME
                        backbone network for testing.
  --checkpointspath CHECKPOINTSPATH
                        checkpoints you want to use.
  --nmsthresh NMSTHRESH
                        thresh used in nms.
cmd example:
CUDA_VISIBLE_DEVICES=0 python test.py --checkpointspath faster_res50_trainbackup_coco/epoch_12.pth --datasetname coco --backbonename resnet50

Demo

usage: demo.py [-h] --imagepath IMAGEPATH --backbonename BACKBONENAME
               --datasetname DATASETNAME --checkpointspath CHECKPOINTSPATH
               [--nmsthresh NMSTHRESH] [--confthresh CONFTHRESH]
optional arguments:
  -h, --help            show this help message and exit
  --imagepath IMAGEPATH
                        image you want to detect.
  --backbonename BACKBONENAME
                        backbone network for demo.
  --datasetname DATASETNAME
                        dataset used to train.
  --checkpointspath CHECKPOINTSPATH
                        checkpoints you want to use.
  --nmsthresh NMSTHRESH
                        thresh used in nms.
  --confthresh CONFTHRESH
                        thresh used in showing bounding box.
cmd example:
CUDA_VISIBLE_DEVICES=0 python demo.py --checkpointspath faster_res50_trainbackup_coco/epoch_12.pth --datasetname coco --backbonename resnet50 --imagepath 000001.jpg

Reference

[1]. https://github.com/jwyang/faster-rcnn.pytorch
[2]. https://github.com/open-mmlab/mmdetection