Method to Train Model
AssigneeSI Analytics Co., Ltd.
InventorsHakjin LEE, Jamyoung KOO
Life of this patent
Estimating the term…
Abstract
According to an exemplary embodiment of the present disclosure, disclosed is a method to train a model. The method to train the model may include: computing an input image using a neural network model; computing an accuracy of a prediction box by comparing the prediction box output from the neural network model and a ground truth box; and training the neural network model by performing backpropagation on the neural network model based on the accuracy.
Claims11
A method to train a model, the method comprising:
- computing an input image including at least one rotated object using a neural network model;
- determining a first reference point and a second reference point by comparing a prediction box output from the neural network model and a ground truth box;
- computing an accuracy of the prediction box based on the first reference point and the second reference point;
- and
- training the neural network model by performing backpropagation on the neural network model based on the accuracy.
A computer program stored in a computer readable medium wherein when the computer program is executed in one or more processors, the computer program performs the following operations for model training, the operations comprising:
- computing an input image containing at least one rotated object using a neural network model;
- determining a first reference point and a second reference point by comparing a prediction box output from the neural network model and a ground truth box;
- computing an accuracy of the prediction box based on the first reference point and the second reference point;
- and
- training the neural network model by performing backpropagation on the neural network model based on the accuracy.
A server, comprising:
- processor with one or more cores;
- a network unit;
- and
- a memory;
- wherein the processor is configured to:
- compute an input image containing at least one rotated object using a neural network model;
- determine a first reference point and a second reference point by comparing a prediction box output from the neural network model and a ground truth box;
- compute an accuracy of the prediction box based on the first reference point and the second reference point;
- and
- train the neural network model by performing backpropagation on the neural network model based on the accuracy.
Dependencies are inferred from the claim text.