Recently, I'm trying to optimize this project. Maybe about one or more weeks, I will upload the newest results and provide a better version of train, test, eval codes.

I wish this version of yolo can help everyone who is interested in Object Detection.

I am trying to build a better YOLOv3 with SPP, PAN and more other modules and tricks.

https://github.com/yjh0410/yolov3-plus_PyTorch

I sincerely recommend everyone to use my new project. It is better~

In this project, you can enjoy:

• YOLOv2
• YOLOv3
• YOLOv3Spp
• SlimYOLOv2
• TinyYOLOv3

What I have to say is that I don't try to 100% reproduce the whole official YOLO project, because it is really hard to me. I have not much computation resource, so I can't train my yolov3 on COCO. It will cost more than two weeks...

Recently, I made some improvement, and my yolo project is very close to official yolo models.

I will upload the new model again. Just hold on~

However, I have a qeustion: Is the mAP metric really good? Does it really suit object detection?

I find higher mAP doesn't mean better visualization...so weird.

For backbone, I provide the link to download them in backbone\weights\README.md.

For detectors, you can find the link to download them in weights\README.md.

Tricks in official paper:

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

First of all, I reproduce the backbone darknet19 on ImageNet.

Not bad, right?

Then I train my YOLOv2 on VOC dataset. I really enjoy yolo. It is so amazing! I think I have achieved this goal:

With 160 training epochs, my yolo-v2 only gets 76.0 mAP with 416 input size and 77.0 mAP with 544 input size. To be better, I add another 90 epochs. With 250 training epochs, my yolo-v2 performs very well !

During testing stage, I set conf thresh as 0.001 and set nms thresh as 0.5 to obtain above results. To make my model faster, I set conf thresh as 0.01. With this higher conf thresh, my yolo-v2 still performs very well and gets 77.0 mAP with 416 input size and 78.0 mAP with 544 input size.

I visualize some detection results whose score is over 0.3 on VOC 2007 test:

COCO:

I train my YOLOv2 with 250 epochs on COCO. From the above table, my YOLOv2 is better, right?

In TITAN Xp, my yolo-v2 runs at 100+ FPS, so it's very fast. I have no any TITAN X GPU, and I can't run my model in a X GPU. Sorry, guys~

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.

First of all, I try to reproduce darknet53 on ImageNet:

Not so bad, right?

Then, I train my YOLOv3 on VOC:

I use the same training strategy to my YOLOv2. My data-processing code is a little different from official YOLOv3. For more details, you can check my code files.

COCO:

Original YOLOv3:

Our YOLOv3_PyTorch:

My YOLOv3 is very stronger and better, right?

I also visualize some detection results whose score is over 0.3 on COCO 2017-val:

So, just have fun !

On COCO:

My YOLOv3SPP is not better than my YOLOv3. It is a little confusing ...

I build a very simple lightweight backbone: darknet_tiny

I replace the darknet19 used in YOLOv2 with darknet_tiny.

My SlimYOLOv2 is fast and strong. On VOC, it gets 70.7 mAP and 100+ FPS on 1660ti GPU.

Just enjoy it.

And, I'm still trying to make it faster without too much drop of precision.

We evaluate our TinyYOLOv3 on COCO-val with inputsize 608:

• 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

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

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