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1122 · Jun 202219922001200920172026
3 results for YOLOv4

New methods detect objects in industrial settings with little training data.

problem Lack of training data limits object detection in industrial settings.
method Adapted Faster R-CNN and Scaled Yolov4-p5 architectures for small training data.
result Both models can distinguish unknown objects from homogeneous backgrounds.

We propose Mish\textit{Mish}, a novel self-regularized non-monotonic activation function which can be mathematically defined as: f(x)=xtanh(softplus(x))f(x)=x\tanh(softplus(x)). As activation functions play a crucial role in the performance and training dynamics in neural networks, we validated experimentally on several well-known benchmarks…

2019-08-23abs ↗pdf ↗