DarkNet-53 convolutional neural network - MATLAB darknet53 (2024)

DarkNet-53 convolutional neural network

Since R2020a

DarkNet-53 is a convolutional neural network that is 53 layers deep. You can load a pretrained version of the network trained on more than a million images from the ImageNet database [1]. The pretrained network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. As a result, the network has learned rich feature representations for a wide range of images. The network has an image input size of 256-by-256. For more pretrained networks in MATLAB^®, see Pretrained Deep Neural Networks.

You can use classify to classify new images using the DarkNet-53 model. Follow the steps of Classify Image Using GoogLeNet and replace GoogLeNet with DarkNet-53.

To retrain the network on a new classification task, follow the steps of Train Deep Learning Network to Classify New Images and load DarkNet-53 instead of GoogLeNet.

DarkNet-53 is often used as the foundation for object detection problems and YOLO workflows [2]. For an example of how to train a you only look once (YOLO) v2 object detector, see Object Detection Using YOLO v2 Deep Learning. This example uses ResNet-50 for feature extraction. You can also use other pretrained networks such as DarkNet-19, DarkNet-53, MobileNet-v2, or ResNet-18 depending on application requirements.

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darknet53

darknet53

ans = DAGNetwork with properties: Layers: [184×1 nnet.cnn.layer.Layer] Connections: [206×2 table] InputNames: {'input'} OutputNames: {'output'}

deepNetworkDesigner(darknet53)

net = darknet53

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