This repo contains many object detection methods that aims at single shot and real time, so the speed is the only thing we talk about. Currently we have some base networks that support object detection task such as MobileNet V2, ResNet, VGG etc. And some SSD variants such as FSSD, RFBNet, Retina, and even Yolo are contained.
If you have any faster object detection methods welcome to discuss with me to merge it into our master branches.
Work are just being progressing. Will update some result and pretrained model after trained on some datasets. And of course, some out-of-box inference demo.
[updates]:
2018.11.06: As you know, after trained fssd_mobilenetv2 the inference codes actually get none result, still debugging how this logic error comes out.
All settings about base net and ssd variants are under ./experiments/cfgs/*.yml, just edit it to your enviroment and kick it off.
python3 train.py --cfg=./experiments/cfgs/rfb_lite_mobilenetv2_train_vocyml
You can try train on coco first then using your custom dataset. If you have your coco data inside /path/to/coco, the just link it to ./data/ and you can find coco inside ./data. Same as VOC data.
That is what it trains like. After that I shall upload some trained model.
To predict on a simple image, you can find some useful codes in demo_simpler.py. But it still under testing. I will upload some images when I get it predicted success.
This version contained by myself and portable to pytorch newest version. As well as some pretrained model and speed test benchmark. If you have any question or want ask Computer Vision questions you can contact me via wechat: jintianiloveu.
Some useful links and other repo: