problem description

recently, using TensorFlow training samples, the neural network chooses that the yolov3,loss value has been maintained at about 8, and there is no global convergence, and the final recognition rate is not high.

the environmental background of the problems and what methods you have tried

the loss of 8 is too large, so I want to optimize it, because I have little experience in machine learning, and I only summarize the following optimization points:

• modify the activation function
• modify the loss function
• optimize training samples: increase the number of training sets; reduce interference items when marking

apart from these points, where else can we optimize? For the time being, I haven"t thought about setting the super-parameter, such as the step size when the gradient drops, and so on.