![]() It will install core and some helper libraries into your local environment needed to use a TF2 Object Detection API and take care of your training dataset. copy object_detection\exporter_main_v2.py. python setup.py install python object_detection\builders\model_builder_tf2_test.py conda install imutils pdf2image beautifulsoup4 typeguard pip install tf-image copy object_detection\model_main_tf2.py. copy object_detection\packages\tf2\setup.py. From your terminal window run one-by-one the following commands: # from conda create -n \ python=3.7 \ tensorflow=2.3 \ numpy=1.17.4 \ tf_slim \ cython \ git conda activate git clone pip install git cd models\research # from \models\researchprotoc object_detection\protos\*.proto - python_out=. Under a path of your choice create a new folder, that we will refer to hereinafter as a ‘project’s root folder’. Installation and setup of TF2 Object Detection API ![]() Meanwhile please refer to the transfer-learning flow-chart (see in interactive view) of an object detector for an arbitrary new class: Hereinafter we will touch upon performing non-maximum suppression for this purpose. Whatever be the choice it will put you further to an issue of overlapping bounding boxes. ![]() Thus you will benefit from its complete end-to-end trainable architecture. It is further said in the abovementioned post that “another approach is to treat a pre-trained classification network as a base (backbone) network in a multi-component deep learning object detection framework (such as Faster R-CNN, SSD, or YOLO)”. Despite its proven efficiency, this two-stage object detection paradigm, known as R-CNN, still relies on heavy computations and is not suitable for real-time application. ![]() Within this method, you are free to decide whether to use a traditional ML algorithm for image classification ( utilising or not CNN as a feature extractor) or train a simple neural network to handle arbitrary large datasets. One approach to build a custom object detector, as he suggests, is to choose any classifier and precede it with an algorithm to select and provide regions of an image that may contain an object. ![]() Adrian Rosebrock, a known CV researcher, states in his “ Gentle guide to deep learning object detection” that: “object detection, regardless of whether performed via deep learning or other computer vision techniques, builds on image classification and seeks to localize precisely an area where an object appears”. ![]()
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