In this project, you can enjoy:

• a new version of yolov1

This is a a new version of YOLOv1 built by PyTorch:

• Backbone: resnet18
• Head: SPP, SAM

• Batchsize: 32
• Base lr: 1e-3
• Max epoch: 160
• LRstep: 60, 90
• optimizer: SGD

Before I tell you how to use this project, I must say one important thing about difference between origin yolo-v2 and mine:

• For data augmentation, I copy the augmentation codes from the https://github.com/amdegroot/ssd.pytorch which is a superb project reproducing the SSD. If anyone is interested in SSD, just clone it to learn !(Don't forget to star it !)

So I don't write data augmentation by myself. I'm a little lazy~~

My loss function and groundtruth creator both in the tools.py, and you can try to change any parameters to improve the model.

Environment:

• Python3.6, opencv-python, PyTorch1.1.0, CUDA10.0,cudnn7.5
• For training: Intel i9-9940k, TITAN-RTX-24g
• For inference: Intel i5-6300H, GTX-1060-3g

VOC:

COCO:

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

As for now, I only train and test on PASCAL VOC2007 and 2012.

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
• data/VOCdevkit/VOC2012.

# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2007.sh # <directory>

# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2012.sh # <directory>

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

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

• data/COCO/annotations/
• data/COCO/train2017/
• data/COCO/val2017/
• data/COCO/test2017/

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

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

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

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

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

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

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.