-
公开(公告)号：US20190385059A1
公开(公告)日：2019-12-19
申请号：US16421259
申请日：2019-05-23
Applicant: TuSimple, Inc.
Inventor： Zehao Huang , Naiyan Wang
Abstract: The present disclosure provides a method and an apparatus for training a neural network and a computer server. The method includes: selecting automatically input data for which processing by the neural network fails, to obtain a set of data to be annotated; annotating the set of data to be annotated to obtain a new set of annotated data; acquiring a set of newly added annotated data containing the new set of annotated data, and determining a union of the set of newly added annotated data and a set of training sample data for training the neural network in a previous period as a set of training sample data for a current period; and training the neural network iteratively based on the set of training sample data for the current period, to obtain a neural network trained in the current period.
-
公开(公告)号：US20190384982A1
公开(公告)日：2019-12-19
申请号：US16421320
申请日：2019-05-23
Applicant: TuSimple, Inc.
Inventor： Zehao Huang , Naiyan Wang
Abstract: The present disclosure provides a method and an apparatus for sampling training data and a computer server. The method includes: inputting a video to a target detection model to obtain a detection result for each frame of image; inputting the detection results for all frames of images in the video to a target tracking model, to obtain a tracking result for each frame of image; and for each frame of image in the video: matching the detection result and the tracking result for the frame of image, and when the detection result and the tracking result for the frame of image are inconsistent with each other, determining the frame of image as a sample image to be marked, for which processing by the target detection model is not optimal.
-
Search Results
2
records
(took 0.088 seconds)
