Recently, I have released a new YOLO project:

https://github.com/yjh0410/PyTorch_YOLO_Tutorial

In my new YOLO project, you can enjoy:

• a new and stronger YOLOv1
• a new and stronger YOLOv2
• YOLOv3
• YOLOv4
• YOLOv5
• YOLOv7
• YOLOX
• RTCDet

In this project, you can enjoy:

• YOLOv2 with DarkNet-19
• YOLOv2 with ResNet-50
• YOLOv2Slim
• YOLOv3
• YOLOv3-Spp
• YOLOv3-Tiny

I just want to provide a good YOLO project for everyone who is interested in Object Detection.

Google Drive: https://drive.google.com/drive/folders/1T5hHyGICbFSdu6u2_vqvxn_puotvPsbd?usp=sharing

BaiDuYunDisk: https://pan.baidu.com/s/1tSylvzOVFReUAvaAxKRSwg Password d266

You can download all my models from the above links.

Tricks in official paper:

• batch norm
• hi-res classifier
• convolutional
• anchor boxes
• new network
• dimension priors
• location prediction
• passthrough
• multi-scale
• hi-red detector

I replace darknet-19 with resnet-50 and get a better result on COCO-val

Official YOLOv3:

Our YOLOv3:

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

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.py -d voc --cuda -v [select a model] -hr -ms --ema

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

If you have only one gpu:

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

If you have multi gpus like 8, and you put 4 images on each gpu:

python -m torch.distributed.launch --nproc_per_node=8 train.py -d coco --cuda -v [select a model] -hr -ms --ema \
                                                                        -dist \
                                                                        --sybn \
                                                                        --num_gpu 8\
                                                                        --batch_size 4

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.