DeepLab-v3+, Google’s latest and best performing Semantic Image Segmentation model is now open sourced!
DeepLab is a state-of-the-art deep learning model for semantic image segmentation, with the goal to assign semantic labels (e.g., person, dog, cat and so on) to every pixel in the input image. Assigning these semantic labels sets a much stricter localization accuracy requirements than other visual entity recognition tasks such as image-level classification or bounding box-level detection. Examples of semantic image segmentation tasks include synthetic shallow depth-of-field effect shipped in the portrait mode of the Pixel 2 and Pixel 2 XL smartphones and mobile real-time video segmentation.
DeepLab-v3+ is implemented in TensorFlow and has its models built on top of a powerful convolutional neural network (CNN) backbone architecture for the most accurate results, intended for server-side deployment.
Let’s have a look at some of the highlights of DeepLab v3:
You can read more about this announcement on the Google Research blog.
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