An implementation of Cascade R-CNN: Delving into High Quality Object Detection. I only trained and tested on pascal voc dataset. The source code is here which implemented by caffe and also evalated on pascal voc.

Introduction

As we all know, the cascade structure is designed for R-CNN structure, so i just used the cascade structure based on DetNet to train and test on pascal voc dataset (DetNet is not only faster than fpn-resnet101, but also better than fpn-resnet101).

Based on DetNet_Pytorch, i mainly changed the forward function in fpn.py. It‘s just a naive implementation, so its speed is not fast.

Update

2019/01/01:

Benchmarking

I benchmark this code thoroughly on pascal voc2007 and 07+12. Below are the results:

1). PASCAL VOC 2007 (Train/Test: 07trainval/07test, scale=600, ROI Align)

model(FPN) GPUs Batch Size lr lr_decay max_epoch Speed/epoch Memory/GPU AP AP50 AP75
DetNet59 1 GTX 1080 (Ti) 2 1e-3 10 12 0.89hr 6137MB 44.8 76.1 46.2
DetNet59-Cascade 1 GTX 1080 (Ti) 2 1e-3 10 12 1.62hr 6629MB 48.9 75.9 53.0

2). PASCAL VOC 07+12 (Train/Test: 07+12trainval/07test, scale=600, ROI Align)

model(FPN) GPUs Batch Size lr lr_decay max_epoch Speed/epoch Memory/GPU AP AP50 AP75
DetNet59 1 GTX 1080 (Ti) 1 1e-3 10 12 2.41hr 9511MB 53.0 80.7 58.2
DetNet59-Cascade 1 GTX 1080 (Ti) 1 1e-3 10 12 4.60hr 1073MB 55.6 80.1 61.0

Preparation

First of all, clone the code

git clone https://github.com/guoruoqian/cascade-rcnn_Pytorch.git

Then, create a folder:

cd cascade-rcnn_Pytorch && mkdir data

prerequisites

Data Preparation

Pretrained Model

 You can download the detnet59 model which i trained on ImageNet from:

Compilation

As pointed out by ruotianluo/pytorch-faster-rcnn, choose the right -arch in make.sh file, to compile the cuda code:

GPU model Architecture
TitanX (Maxwell/Pascal) sm_52
GTX 960M sm_50
GTX 1080 (Ti) sm_61
Grid K520 (AWS g2.2xlarge) sm_30
Tesla K80 (AWS p2.xlarge) sm_37

Install all the python dependencies using pip:

pip install -r requirements.txt

Compile the cuda dependencies using following simple commands:

cd lib
sh make.sh

It will compile all the modules you need, including NMS, ROI_Pooing, ROI_Align and ROI_Crop. The default version is compiled with Python 2.7, please compile by yourself if you are using a different python version.

Usage

If you want to use cascade structure, you must set --cascade and --cag in the below script. cag determine whether perform class_agnostic bbox regression.

train voc2007 use cascade structure:

CUDA_VISIBLE_DEVICES=3 python3 trainval_net.py exp_name --dataset pascal_voc --net detnet59 --bs 2 --nw 4 --lr 1e-3 --epochs 12 --save_dir weights --cuda --use_tfboard True --cag --cascade

test voc2007:

CUDA_VISIBLE_DEVICES=3 python3 test_net.py exp_name --dataset pascal_voc --net detnet59 --checksession 1 --checkepoch 7 --checkpoint 5010 --cuda --load_dir weights --cag --cascade

Before training voc07+12, you must set ASPECT_CROPPING in detnet59.yml False, or you will encounter some error during the training.

train voc07+12:

CUDA_VISIBLE_DEVICES=3 python3 trainval_net.py exp_name2 --dataset pascal_voc_0712 --net detnet59 --bs 1 --nw 4 --lr 1e-3 --epochs 12 --save_dir weights --cuda --use_tfboard True --cag --cascade

run demo.py :

Before run demo, you must make dictionary 'demo_images' and put images (VOC images) in it. You can download the pretrained model  listed in above tables.

CUDA_VISIBLE_DEVICES=3 python3 demo.py exp_name2 --dataset pascal_voc_0712 --net detnet59 --checksession 1 --checkepoch 8 --checkpoint 33101 --cuda --load_dir weights --cag --image_dir demo_images --cascade --result_dir vis_cascade