Update

Recently, I optimize my YOLO project:

https://github.com/yjh0410/PyTorch_YOLO-Family

On the COCO-val:

	Backbone	Size	FPS	AP	AP50	AP75	APs	APm	APl	GFLOPs	Params
YOLO-Nano	ShuffleNetv2-1.0x	512		21.6	40.0	20.5	7.4	22.7	32.3	1.65	1.86M
YOLO-Tiny	CSPDarkNet-Tiny	512		26.6	46.1	26.9	13.5	30.0	35.0	5.52	7.66M
YOLOv1	ResNet50	640		35.2	54.7	37.1	14.3	39.5	53.4	41.96	44.54M
YOLOv2	ResNet50	640		36.3	56.6	37.7	15.1	41.1	54.0	42.10	44.89M
YOLOv3	DarkNet53	640		38.7	60.2	40.7	21.3	41.7	51.7	76.41	57.25M
YOLOv4	CSPDarkNet53	640		40.5	60.4	43.5	24.2	44.8	52.0	60.55	52.00M

YOLO-Nano

A new version YOLO-Nano inspired by NanoDet.

In this project, you can enjoy:

• a different version of YOLO-Nano

Network

This is a a different of YOLO-Nano built by PyTorch:

• Backbone: ShuffleNet-v2
• Neck: a very lightweight FPN+PAN

Train

• Batchsize: 32
• Base lr: 1e-3
• Max epoch: 120
• LRstep: 60, 90
• optimizer: SGD

The overview of my YOLO-Nano

Experiment

Environment:

• Python3.6, opencv-python, PyTorch1.1.0, CUDA10.0,cudnn7.5
• For training: Intel i9-9940k, RTX-2080ti

VOC:

YOLO-Nano-1.0x:

	size	mAP
VOC07 test	320	65.0
VOC07 test	416	69.1
VOC07 test	608	70.8

COCO:

	size	AP	AP50	AP75	AP_S	AP_M	AP_L
COCO eval	320	17.2	33.1	16.2	2.6	16.0	31.7
COCO eval	416	19.6	36.9	18.6	4.6	19.1	33.3
COCO eval	608	20.6	38.6	19.5	7.0	22.5	30.7

YOLO-Nano-0.5x:

hold on ...

Visualization

On COCO-val

The overview of my YOLO-Nano

Installation

• Pytorch-gpu 1.1.0/1.2.0/1.3.0
• Tensorboard 1.14.
• opencv-python, python3.6/3.7

Dataset

VOC Dataset

I copy the download files from the following excellent project: https://github.com/amdegroot/ssd.pytorch

I have uploaded the VOC2007 and VOC2012 to BaiDuYunDisk, so for researchers in China, you can download them from BaiDuYunDisk:

Link：https://pan.baidu.com/s/1tYPGCYGyC0wjpC97H-zzMQ

Password：4la9

You will get a VOCdevkit.zip, then what you need to do is just to unzip it and put it into data/. After that, the whole path to VOC dataset is data/VOCdevkit/VOC2007 and data/VOCdevkit/VOC2012.

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>

MSCOCO Dataset

I copy the download files from the following excellent project: https://github.com/DeNA/PyTorch_YOLOv3

Download MSCOCO 2017 dataset

Just run sh data/scripts/COCO2017.sh. You will get COCO train2017, val2017, test2017.

Train

VOC

python train.py -d voc --cuda -v [select a model] -ms

You can run python train.py -h to check all optional argument.

COCO

python train.py -d coco --cuda -v [select a model] -ms

Test

VOC

python test.py -d voc --cuda -v [select a model] --trained_model [ Please input the path to model dir. ]

COCO

python test.py -d coco-val --cuda -v [select a model] --trained_model [ Please input the path to model dir. ]

Evaluation

VOC

python eval.py -d voc --cuda -v [select a model] --train_model [ Please input the path to model dir. ]

COCO

To run on COCO_val:

python eval.py -d coco-val --cuda -v [select a model] --train_model [ Please input the path to model dir. ]

To run on COCO_test-dev(You must be sure that you have downloaded test2017):

python eval.py -d coco-test --cuda -v [select a model] --train_model [ Please input the path to model dir. ]

You will get a .json file which can be evaluated on COCO test server.