Another benefit is that training is more stable for small datasets. This makes training much faster - it is now enough to train for 10-15 iterations to get a good model. Improved: The neural network behind the DL_DetectFeatures tool is now pre-trained with a large database of images.As a result, the tools is much faster (~20% faster on GPU, twice as fast on CPU) while accuracy is preserved or better (depending on the use case). We used a set of 15 industrial and non-industrial use cases as a benchmark, and performed both manual and automatic (ProxylessNAS) architecture optimization. Improved: The architecture of the neural network behind the DL_DetectFeatures tool has been re-optimized. ![]()
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