K210-Yahboom-binds-eyes

Datasets

Thanks to these datasets for acdemical use.

https://paperswithcode.com/datasets?q=traffic&v=lst&o=match or pedestrian, bike, bicycle.

• https://apolloscape.auto/trajectory.html

• https://data.mendeley.com/datasets/766ygrbt8y/3

• http://www.cbsr.ia.ac.cn/users/sfzhang/WiderPerson/

• https://www.kaggle.com/datasets/pengcw1/market-1501

• https://www.crcv.ucf.edu/data/UCF50.php

Training

• model.py and the cost-free cloud training from https://maixhub.com.

!! https://developer.canaan-creative.com/ may also provide similar services.

You can try tfjs model from https://maixhub.com/phone/deploy/model?type=classification&token=dcc525d655164c06aab4b8615c4145ab.

1. 50342 (Too large to load for the standard microPython installation)

+ mirror, + rotation, - blur, <contain>, 224, 224, Avg = 123.5, Std = 58.395
mobilenet_1.0
Epoches = 180, Batch = 128, Rate = .001, + data_balance
ValLoss = .18418, ValAcc = 1.0

2. 50343 (Too large to load for the standard microPython installation)

+ mirror, + rotation, - blur, <fill>, 224, 224, Avg = 123.5, Std = 58.395
mobilenet_1.0
Epoches = 160, Batch = 128, Rate = .001, + data_balance
ValLoss = .44987, ValAcc = .79167

3. 50627 (SELECTED)

+ mirror, + rotation, - blur, <fill>, 224, 224, Avg = 123.5, Std = 58.395
mobilenet_0.75
Epoches = 100, Batch = 64, Rate = .001, + data_balance
ValLoss = .37415, ValAcc = .91667

4. 50628

+ mirror, + rotation, - blur, <contain>, 224, 224, Avg = 123.5, Std = 58.395
mobilenet_0.5
Epoches = 100, Batch = 128, Rate = .001, + data_balance
ValLoss = .66497, ValAcc = .70833

5. 50629

+ mirror, + rotation, - blur, <fill>, 224, 224, Avg = 123.5, Std = 58.395
mobilenet_0.25
Epoches = 100, Batch = 32, Rate = .001, - data_balance
ValLoss = .58333, ValAcc = .89375

It seems that blurring may contributes to better recognition.