YiLiangNie / Focal-Loss

loss layer of implementation

Focal-Loss

loss layer of implementation.
You can see "Focal Loss for Dense Object Detection" arXiv for more information.

Usage

// Focal Loss layer
optional FocalLossParameter focal_loss_param = 124;

// Focal Loss for Dense Object Detection
message FocalLossParameter {
  enum Type {
    ORIGIN = 0; // FL(p_t)  = -(1 - p_t)^gama * log(p_t), where p_t = p if y == 1 else 1 - p, whre p = sigmoid(x)
    LINEAR = 1; // FL*(p_t) = -log(p_t) / gama, where p_t = sigmoid(gama * x_t + beta), where x_t = x * y, y is the ground truth label {-1, 1}
  }
  optional Type type   = 1 [default = ORIGIN]; 
  optional float gamma = 2 [default = 2];
  // cross-categories weights to solve the imbalance problem
  optional float alpha = 3 [default = 0.25]; 
  optional float beta  = 4 [default = 1.0];
}

Derivative

see https://github.com/zimenglan-sysu-512/paper-note/blob/master/cross_entropy_loss.pdf

ToDo

Implement alpha

Notice

Here use softmax instead of sigmoid function.
If you want see how to use sigmoid to implement Focal Loss, please see https://github.com/sciencefans/Focal-Loss to get more information.