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Comparison of popular CNN architectures. The vertical axis shows top 1 accuracy on ImageNet classification. The horizontal axis shows the number of operations needed to classify an image. Circle size is proportional to the number of parameters in the network. 

Comparison of popular CNN architectures. The vertical axis shows top 1 accuracy on ImageNet classification. The horizontal axis shows the number of operations needed to classify an image. Circle size is proportional to the number of parameters in the network. 

Source publication
Conference Paper
Full-text available
Can convolutional neural networks (CNNs) recognize gestures from a camera for robotic control? We examine this question using a small set of vehicle control gestures (move forward, grab control, no gesture, release control, stop, turn left, and turn right). Deep learning methods typically require large amounts of training data. For image recognitio...

Contexts in source publication

Context 1
... we limit ourselves to CNN architectures which we expect to fit in memory of a Jetson and run in real time. Canziani et al. [5] provide an excellent comparison of popular CNN architectures shown in Figure 5. Initially we identified AlexNet [6] as the most promising network to run in real time on a Jetson TX1. ...
Context 2
... found that ResNet-50 will run at close to 10 frames per second on the TX2. This architecture provides a good tradeoff between computation time and accuracy on ImageNet (shown in Figure 5). ...

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