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...)
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
MoileNet : 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