Custom Object Detection with Deep Learning - Using TensorFlow Lite on Small Computers
Been learning convolutional neural network principles and deep learning for a few months. Here is a demonstration of training an existing deep learning network - SSD Mobilenet V2*, for custom object detection using TensorFlow framework (Using V2.6 at present). By using the method of 'transfer learning', I trained the network to detect tennis ball and geared DC motor, using a small datasets of 96 photos.
*Single-shot detect, a popular one-stage object detection deep neural network pipeline, which has a feature extraction front-end, and a backup that performs classification and localization of objects. For this instance, the feature extraction front-end is using Mobilenet V2 backbone ('backbone' means a sequence of neural-network layers).
In the video I show the comparison of running the model on TensorFlow Lite interpreter on a standard laptop with Intel Core i5 processor (no GPU) with 12GB of RAM and Raspberry Pi 4B (4GB of RAM). The Raspberry Pi 4B can only manage around 2-3 frames per second. I am considering adding a USB accelerator (like Coral edge TPU processor) to speed it up.
All the procedures are learnt from the official object detection tutorial found in:
https://tensorflow-object-detection-a...
and TensorFlow Lite guide: https://www.tensorflow.org/lite/guide....
On this page of the site you can watch the video online Custom Object Detection with Deep Learning - Using TensorFlow Lite on Small Computers with a duration of hours minute second in good quality, which was uploaded by the user fkungms 08 October 2021, share the link with friends and acquaintances, this video has already been watched 231 times on youtube and it was liked by 5 viewers. Enjoy your viewing!
