SBD is an anchor free object detector that combines the advantages of YOLO and Centernet,

Designed specifically to operate on edge devices, and is light, fast, and accurate

Quick Start🚀

Installation

git clone https://github.com/inzapp/sbd

# local installation
cd sbd
python -m pip intall setup/requirements_cuda_xxx.txt

# docker installation
docker pull inzapp/sbd:cu118 # in case of cuda version 11.8
cd sbd/setup
./run_docker.sh

Train

python train.py --cfg cfg/cfg.yaml

Detect

# need hardware monitor or X11 forward for CLI system
cd checkpoint/model_name/model_type/
python ../../../detect.py # detect with validation data path in cfg.yaml
python ../../../detect.py --dataset train # detect with train data path in cfg.yaml
python ../../../detect.py --path "/your/images/path/dir" # user defined image path dir
python ../../../detect.py --path "/your/images/path/image.jpg" # one image detection
python ../../../detect.py --path "/your/video/path.mp4" # realtime video detection
python ../../../detect.py --path "rtsp://foo/bar" # rtsp stream realtime detection
python ../../../detect.py --path "rtsp://user:passsword@foo/bar" # case need authentication

mAP

cd checkpoint/model_name/model_type/
python ../../../map.py # calculate mAP with validation data in cfg.yaml
python ../../../map.py --dataset train # calculate mAP with train data in cfg.yaml
python ../../../map.py --conf 0.1 --iou 0.6 --cached # fast calculation using cached csv file for --conf, --iou. must run map.py at least once

Auto label

cd checkpoint/model_name/model_type/
python ../../../auto_label.py --path "/your/image/path/dir" --conf 0.3 # save label with predicted result

Multi GPU training

# cfg/cfg.yaml
devices: [] # cpu training
devices: [0] # one gpu training with device index 0
devices: [2, 3] # 2 GPU training with device index 2, 3
devices: [0, 1, 2, 3] # 4 GPU training with device index 0, 1, 2, 3

ONNX export

./export.sh  # just copy and paste your model path for exporting

Introduction

SBD is a light and fast object detector

This can be particularly useful in edge devices

Because there are several layers that are not supported by most edge devices, SBD uses a backbone that is only used as a light csp block consisting of a vanilla convolution layer

Because fast processing is the primary purpose of SBD, post processing is also very concise and fast

• Totally anchor free
• Low memory required
• ALE loss with convex IoU loss function
• No mathematical operations for post processing
• Customized learning rate scheduler
• Lightweight CSP block
• Lightweight FPN head
• Virtual anchor training
• Multi scale training
• Various output resolution
• Heatmap ignore trick
• Support SBD-P6 model
• Multi GPU training
• Fast training loop

Model

SBD provides two types of models: one output layer model and multi layer output model

And you can choose the output resolution scale by changing model_type in cfg.yaml file

model_type: m1p2 # medium backbone one output layer model with pyramid scale 2

The description of the model_type is as follows

m : backbone type, available backbones : n(nano), s(small), m(medium), l(large), x(x-large)

1 : 1 for one output layer model or m for multi layer model

p : constant character for naming rule

2 : pyramid scale value, 2 ~ 6(p6 only) is available

available model type examples) n1p5, nmp3, s1p2, smp4, m1p3, mmp2, l1p4, lmp2, x1p5, xmp3

You can change the resolution of the output layer by changing the pyramid scale

Max output layer resolution is divided by 2^p of input resolution

When using the multi layer model, the number of output layers depends on the paramid scale

Through the FPN head until the paramid scale is reached,

output layers are added one by one from the lowest resolution layer

For better understanding, take a model with model_type m1p2 as an example

In most cases, 1p2 model type is recommended

If the input resolution is very large, the pyramid scale of 2 has a very large output resolution

In this case, you can test a p2 or lower pyramid scale to reduce post processing time

Also, if the model learns from simple data and results in a high mAP

You can try lowering the paramid scale to save post processing time

If the box size of the train data is very small to very large (like COCO), mp2 models can be helpful

P6

If you want to use p6 model, modify cfg.yaml as below

p6_model: True

p6 model has addtional downscaling block and use 64 strides so input resolution must be multiple of 64

6 pyramid scale is available only when p6_model is True

If p6_model: False in cfg.yaml, p5(32 strides) is used by default

When using the multi layer model,

one output layer is added to reach the pyramid scale as the downscale block is added

Virtual anchor

The virtual anchor is extracted from train data by K-means clustering

Each output layer has one clustered box as an anchor,

which determines the index of the output layer on which the object is to be learned by comparing IoU

Since it is used only during training and not during interference,

there is no need to save the value of the virtual anchor separately

Scale constraint

When training the multi layer model, one object is assigned to one output layer

This is called scale constraint, and scale constraint allow the model to train multi scale

If the iou between virtual anchors is high, the scale constraint can degrade the model

You can disable scale constraint by changing the value of va_iou_threshold in the cfg.yaml file

# We recommend va_iou_threshold value to 1.0 as default
# Setting the va_iou_threshold below 1.0 can destabilize training
# Use only if there is a clear reason to lower the va_iou_threshold
va_iou_threshold: 1.0