Don’t follow the TensorFlow docs since they explain how to setup a docker image and compile TF serving that takes forever. We can do much better. Some guy made a docker image with everything already compile on it, so we are going to use that one.
Source: towardsdatascience.com
AI is quickly revolutionizing the security camera industry. Several manufacturers sell cameras which use deep learning to detect cars, people, and other events. These smart cameras are generally expensive though, compared to their “dumb” counterparts.
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While its feature set is geared towards neural networks, deeplearn.js can be described as a general-purpose machine learning framework. Propel is a library for scientific computing that offers automatic differentiation. Gpu.js provides a convenient way to run JavaScript functions on the GPU.
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Today, we are excited to announce the open source release of our latest and best performing semantic image segmentation model, DeepLab-v3+ [1], implemented in Tensorflow. This release includes DeepLab-v3+ models built on top of a powerful convolutional neural network (CNN) backbone architecture [2, 3] for the most accurate results, intended for server-side deployment. As part of this release, we are additionally sharing our Tensorflow model training and evaluation code, as well as models already pre-trained on the Pascal VOC 2012 and Cityscapes benchmark semantic segmentation tasks.
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