Build a Mini-Amazon-Go in 1 Day Nov 2019 Zhi Zhang, Rachel Hu Amazon AI d2l.ai
Outline • Initiate the work-horse(SageMaker notebook) • Showcase a demo from the noob • Sampling real-world data • Hello world for object labeling • Train and Evaluate object detector • Happy hacking! • Leaderboard numpy.d2l.ai/gluon-cv.mxnet.io
Initiate the work-horse *@request-nb.mxnet.io numpy.d2l.ai/gluon-cv.mxnet.io
Showcase a demo from the noob + <= $60 numpy.d2l.ai/gluon-cv.mxnet.io
Showcase a demo from the noob numpy.d2l.ai/gluon-cv.mxnet.io
Showcase a demo from the noob numpy.d2l.ai/gluon-cv.mxnet.io
Sampling real-world data numpy.d2l.ai/gluon-cv.mxnet.io
Hello world for object labeling https://www.makesense.ai/ numpy.d2l.ai/gluon-cv.mxnet.io
Train and Evaluate object detector numpy.d2l.ai/gluon-cv.mxnet.io
Happy Hacking • Team up • Split the tasks • Sampling training images • Parallel labeling • Train and evaluate object detection models • Design your cashier-less logic numpy.d2l.ai/gluon-cv.mxnet.io
Evaluation Method • Accuracy: percentage of correctly detected transaction movements • UX(User experience): how satisfying the overall user experience is. • Innovation: how you bring cool technologies into the pipeline numpy.d2l.ai/gluon-cv.mxnet.io
Troubleshooting • GPU: out of memory • If there are too many notebooks opened which occupies lot’s of GPU memory, then you should close some of them to avoid OOM error numpy.d2l.ai/gluon-cv.mxnet.io
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