Feature Pyramid Network

• Pytorch implementation of Feature Pyramid Network based on VGG16 and ResNet101.
• Supports multi-scale RoI pooling.
• This project is based on simple-faster-rcnn-pytorch, fpn.pytorch, and bounding-box.
• A dockerized test environment is available.

1. Introduction

• Feature Pyramid Network based on VGG16.
  • The two fully connected layers, fc6 and fc7, are converted into convolutional layers as in SSD.
  • The model uses four levels of features (P3, P4, P5, P6) as shown in the image above.
  • Anchor scales are [64^2, 128^2, 256^2, 512^2], and aspect ratios are [0.5, 1, 2].
• Multi-scale RoI pooling.
  • Original FPN pools RoI from single-level features. As shown in the upper right of the image, large-sized RoIs are pooled from small-scale features, while small-sized RoIs are pooled from large-scale features.
  • Multi-scale RoI pooling uses multiple levels of features to pool RoI. Since the original FPN use single-level features, there are three additional levels of features available for RoI pooling. I found that using three levels of features is the most effective in terms of detection performance. You can specify the number of features to use for RoI pooling with --n_features option.

2. Benchmarks

• Detection results on PASCAL VOC 2007 test dataset
  • All models were evaluated using COCO-style detection evaluation metrics.
  • FPN+ is FPN with multi-scale RoI pooling adopted.
  • Learning rate : 0.001 for the first 50k images, 0.0001 for the next 25k; input size: 600px, batch size: 1; weight decay: 0.0005; momentum: 0.9.

Training dataset	Model	AP	[email protected]	[email protected]	AP(s)	AP(m)	AP(l)
VOC 07	Faster R-CNN	35.10	69.85	30.71	4.59	20.77	38.62
VOC 07	FPN	36.28	68.92	33.93	18.06	22.99	37.88
VOC 07	FPN+	39.09	71.18	37.89	16.21	24.65	41.25
VOC 07 + 12	Faster R-CNN	42.73	75.12	42.49	7.79	26.47	46.50
VOC 07 + 12	FPN	43.69	75.43	44.79	18.32	28.99	45.49
VOC 07 + 12	FPN+	45.35	76.03	47.30	18.04	30.69	47.51

3. Requirements

• Python 3.8.0
• Pytorch 1.7.1 (CUDA 10.2)
• OpenCV
• tqdm
• torchnet
• pycocotools
• scikit-image

4. Usage

4.1. Data preparation

• Download the training, validation, and test data.

# VOC 2007 trainval and test datasets
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
# VOC 2012 trainval dataset
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar

• Extract all of these tars into one directory named VOCdevkit

# VOC 2007 trainval and test datasets
tar xvf VOCtrainval_06-Nov-2007.tar
tar xvf VOCtest_06-Nov-2007.tar
# VOC 2012 trainval dataset
tar xvf VOCtrainval_11-May-2012.tar

• It should have the structure as below. If you want to use something other than "dataset" as the directory name, you should specify it in the --dataset option.

project
├── dataset
│   ├── COCO
│   │   ├── annotations
│   │   │   └── deprecated-challenge2017
│   │   └── images
│   │       ├── test2017
│   │       ├── train2017
│   │       ├── unlabeled2017
│   │       └── val2017
│   └── VOCdevkit
│       ├── VOC2007
│       │   ├── Annotations
│       │   ├── ImageSets
│       │   ├── JPEGImages
│       │   ├── SegmentationClass
│       │   └── SegmentationObject
│       └── VOC2012
│           ├── Annotations
│           ├── ImageSets
│           ├── JPEGImages
│           ├── SegmentationClass
│           └── SegmentationObject
└── feature_pyramid_network
    ├── data
    ├── models
    │   ├── rpn
    │   └── utils
    ├── model_zoo
    └── utils

4.2. Build and run the docker image

I will assume the current working directory is "project" as shown in the above code fence.

• Build the dockerfile (Skip this part if you want to use the pre-built docker image).

docker build -t stnamjef/pytorch-fpn:1.0 ./feature_pyramid_network

• Run the docker image.

# run the docker image
docker run -it -v $(pwd):/workspace --gpus all --ipc host stnamjef/pytorch-fpn:1.0

4.3. Train models

Now the current working directory is "/workspace/feature_pyramid_network" in the docker container.

• Ex 1) FPN based on VGG16 (default model)

python3 ./train.py --model=fpn --backbone=vgg16 --n_features=1 --dataset=voc07

• Ex 2) FPN based on ReNet101

python3 ./train.py --model=fpn --backbone=resnet101 --n_features=1 --dataset=voc07

• Ex 3) FPN with multi-scale RoI pooling (three features)

python3 ./train.py --model=fpn --backbone=vgg16 --n_features=3 --dataset=voc07

• Ex 4) Faster R-CNN based on VGG16 (currently Faster R-CNN does not support ResNet101)

python3 ./train.py --model=frcnn --backbone=vgg16 --dataset=voc07

4.4. Test models

File (pretrained weights) naming format: "model_backbone_nfeatures.pth".

• Ex 1) FPN based on VGG16 (file name: "fpn_vgg16_1.pth")

python3 ./test.py --model=fpn --backbone=vgg16 --n_features=1 --dataset=voc07 --save_dir=./model_zoo

• Ex 2) Faster R-CNN based on VGG16 (file name: "frcnn_vgg16.pth")

python3 ./test.py --model=frcnn --backbone=vgg16 --dataset=voc07 --save_dir=./model_zoo

4.5. Plot predictions

All plots will be saved in the "./results" folder.

• Ex) FPN based on VGG16

# plot predictions for the first 10 images
python3 ./plot.py --model=fpn --backbone=vgg16 --n_features=1 --dataset=voc07 --save_dir=./model_zoo --n_plots=10

5. CLI options

Options	dtype	description
--model	string	Model name (options: frcnn, fpn; default: fpn)
--backbone	string	Backbone network (options: vgg16, resnet101; default: vgg16)
--n_features	int	The number of features to use for RoI poolig (default: 1)
--dataset	string	Dataset name (options: voc07, voc0712, coco; default: voc07)
--data_dir	string	Dataset directory (default: ../dataset)
--save_dir	string	Saving directory (default: ./model_zoo)
--min_size	int	Minimum size of input image (default: 600)
--max_size	int	Maximum size of input image (default: 1000)
--n_workers_train	int	The number of workers for a train loader (default: 8)
--n_workers_test	int	The number of workers for a test loader (default: 8)
--lr	float	Learning rate (default: 1e-3)
--lr_decay	float	Learning rate decay (default: 0.1; 1e-3 -> 1e-4)
--weight_decay	float	Weight decay (default: 5e-4)
--epoch	int	Total epochs (default: 15)
--epoch_decay	int	The epoch to decay learning rate (default: 10)
--nms_thresh	float	IoU threshold for NMS (default: 0.3)
--score_thresh	float	BBoxes with scores less than this are excluded (default in train and test mode: 0.05; default in plot mode: 0.6)
--n_plots	int	The number of images to plot predictions (default: -1; all images)