A better PyTorch version of YOLOv3.

I call it "YOLOv3-plus"~

It is not the final version. I'm still trying to make it better and better.

In this project, you can enjoy three basic detectors:

• yolo-v3-spp
• yolo-v3-plus
• yolo-v3-slim

What's more, I also provide more excellent detector (still be training...) with several kinds of CSPDarknet:

• yolo-v3-plus-large (with CSPDarknet-large)
• yolo-v3-plus-medium (with CSPDarknet-medium)
• yolo-v3-plus-small (with CSPDarknet-small)
• yolo-v3-slim-csp (with CSPDarknet-slim / CSPDarknet-tiny )

Of course, the CSPDarknets used in these new models are all trained by myself on ImageNet. My CSPDarknet is a little different from the one used in YOLOv4 and YOLOv5. I referred to YOLOv4, YOLOv5 and Scaled-YOLOv4. For more details, you can read my backbone files in backbone\cspdarknet.py.

I try to reproduce YOLOv3 with SPP module.

VOC:

COCO:

So, just have fun !

I add PAN module into the above YOLOv3-SPP, and get a better detector:

On COCO eval:

I also provide a lightweight detector: YOLOv3-Slim.

It is very simple. The backbone network, darknet_tiny, consists of only 10 conv layers. The neck is SPP same to as the one used in my YOLOv3-Plus. And the head is FPN+PAN with less conv layers and conv kernels.

COCO eval:

On COCO (please hold on ...)

• 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 and 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.

python train_voc.py -v [select a model] -hr -ms --cuda

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

python train_coco.py -v [select a model] -hr -ms --cuda

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

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

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

To run on COCO_val:

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

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

python eval_coco.py -v [select a model] --train_model [ Please input the path to model dir. ] --cuda -t

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

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