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zimenglan-sysu-512
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Description

loss layer of implementation

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# 39,175
Python
C++
one-sta...
pytorch
27 commits

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]; }

layer { name: "loss_cls" type: "FocalLoss" bottom: "cls_score" bottom: "labels" propagate_down: 1 propagate_down: 0 top: "loss_cls" include { phase: TRAIN } loss_weight: 1 loss_param { ignore_label: -1 normalize: true } focal_loss_param { alpha: 0.25 gamma: 2 } }

Derivative

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

Done

All categories share the same

alpha
.

Sigmoid Form

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.

MXNet Repo

https://github.com/unsky/focal-loss

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