Video is from free to use https://www.pexels.com/video/a-day-in-the-park-1466210/
I set threshold 0.9 to ignore wrong detection but usually thresh=0.6 So Please do not try this at home(this doesnt affect loss or map at all)
This code was tested with Keras
v2.1.5, Tensorflow
v1.6.0 GTX1080
Tensorflow・Keras・Numpy・Scipy・opencv-python・pillow・matplotlib・h5py
https://drive.google.com/drive/u/0/folders/1F8GjD3BFhf_hv9Ipez0twRptYc3P8YwP
Please write loss, acc and if possible mAp and your name if you want as your weight name https://drive.google.com/drive/folders/1u-INV0pNjSjwNgbupXVpr1lwEsTMKW3F?usp=sharing
As the truely perfect model doesn't exist forever there is still a way better. (currently I don't have enought time to search very deep into details too...)
I use this for a detection of few categories and simple shape detection (for my purpose) but weak for coco or voc. This repository is just for my study of network architecture. So there is no measurement but for gif so dont trust too much. If you would like to use better and prooved one ,please use official version of ssd or yolo. If you want to study architecture itself without training technic ,this repo will be good to be as one of many references.
SSD : https://github.com/rykov8/ssd_keras/blob/master/ssd.py
Caffe : https://github.com/weiliu89/caffe/tree/ssd
SSD : https://arxiv.org/abs/1512.02325
FSSD : https://arxiv.org/abs/1712.00960
FFSSD : https://arxiv.org/abs/1712.00960
DSSD : https://arxiv.org/abs/1701.06659
VGG : https://arxiv.org/abs/1409.1556
MobileNet : https://arxiv.org/abs/1704.04861
MobileNetV2 : https://arxiv.org/abs/1801.04381
Xception : https://arxiv.org/abs/1610.02357
MobileNetSSD : https://github.com/chuanqi305/MobileNet-SSD
MobileNetV2-SSDLite : https://github.com/chuanqi305/MobileNetv2-SSDLite
VGG16-SSD : https://qiita.com/tanakataiki/items/226c2460738361d2c4eb
MobileNet-SSD : https://qiita.com/tanakataiki/items/41509e1b0f4a9dcd01b1
FeatureFused-SSD : https://qiita.com/tanakataiki/items/36e71e7d2f5705bd98bb
Xception-SSDLite : https://qiita.com/tanakataiki/items/63fa46f529174d8e4c03
The MIT License (MIT)
Copyright (c) 2018 Taiki Tanaka