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![]() Support for segmentation networks has been added. You can now segment images using tensorflow into grayscale images that show a heatmap per class you are looking for. The segment() method will return a list of images of these grayscale heat maps. detect() will then do all the above but internally run find_blobs() on the heat maps to return instead a list of lists, where each sub list is the blobs detected per class. EdgeImpulse will have support for running segmentation networks thus enabling object detection and localization on Cortex-M processors. * PYTF now uses the optimal amount of memory for buffers versus all - buffers are placed in SRAM if they fit producing a massive speed boost. * Custom scaled/offset outputs now work. * Updated to the latest tensorflow library. * You have access to all input/output model parameters. * Person detection is now int8 and blazing fast - 20 FPS on the Arduino Portena. * Added m55 libs (m0plus libs coming soon once EdgeImpulse adds support for them in the tensorflow make file) * Classify/Segment/Detect work on all image types directly (JPG/BAYER/YUV/RGB565/GRAYSCALE/BINARY) |
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