[OpenR8 solution] Image-Object-Detection-MobileNetV2-SSD300-Caffe (Using Caffe SSD300 for object detection on PCB)

MobileNetV2-SSD300.png

 

This Image-Object-Detection-MobileNetV2-SSD300-Caffe is based on the deep learning Caffe framework. The SSD (Single Shot MultiBox Detector) algorithm is used to train the model. Then the trained model is used to detect the capacitance on the PCB. The training image size is 300 × 300. Unlike other SSD solutions, this solution uses the MobileNetV2 network.

 

First, we need to prepare the image we want the model to learn, label the target in the image, and label the bounding box as a category. The purpose is to let the model know what the object in the image belongs to things. Then, through a series of py files, generate two txt manifest files, so that the model knows which files to train and test. Continue to execute the py file for training. After the training is complete, select a different py file based on the test image you are testing.

 

OpenR8 solution-English-Image-Object-Detection-MobileNetV2-SSD300-Caffe Using MobileNetV2 algorithm for object detection on PCB -20190930.pdf


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