The deep learning based neural networks are able to recognize object classes for one or more given input photos. They can recognize 1000 different object classes. There are four models, mobilenet-V1, mobilenet-V2, Resnet-50, and Inception-V3, in our benchmarking App. Individually, we provide one float model(FP 32) and one quantized model(INT 8) for each network.
The task evaluates whether you can perform auto background removing for exchange scene on your phone. It can recognize 20 different object classes and segment the recognized object using different colors. The model is resnet-50 with atrous convolution layers embedded. Now, we only provide a float model.
Now, you can perform object counting on your phone. The model we use is a combination of mobilenet, a light-weight classification model, and single shot multibox detector(SSD), an object detector doesn’t require resampling pixels or feature maps for bounding box hypotheses, can detect 80 different object classes. It improves in speed for high-accuracy detection. We also provide float models and quantized models for this application.