Training Models for Object Detection
AssigneeHewlett-Packard Development Company, L.P.
InventorsQian Lin, Augusto Valente, Otavio Gomes
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Abstract
In some examples, a computing device can include a processing resource and a memory resource storing instructions to cause the processing resource to cause a convolutional neural network (CNN) model to be trained with an initial training data set to detect an object included in annotated images included in the initial training data set, cause the trained CNN model to perform inferencing on unannotated images included in an inference data set to detect the object in the unannotated images, determine an error rate of the trained CNN model, and cause the trained CNN model to be further trained based on the error rate.
Claims15
A computing device, comprising:
- a processor resource;
- and
- a non-transitory memory resource storing machine-readable instructions stored thereon that, when executed, cause the processor resource to:
- cause a convolutional neural network (CNN) model to be trained with an initial training data set to detect an object included in annotated images included in the initial training data set;
- cause the trained CNN model to perform inferencing on unannotated images included in an inference data set to detect the object in the unannotated images;
- determine an error rate of the trained CNN model, wherein the error rate is a rate of misdetection of the object in the unannotated images;
- and
- cause the trained CNN model to be further trained based on the error rate.
A non-transitory machine-readable storage medium storing machine-readable instructions stored thereon that, when executed, cause a processor resource to:
- cause a convolutional neural network (CNN) model to be trained with an initial training data set to detect an object included in annotated images included in the initial training data set;
- cause the trained CNN model to perform inferencing on unannotated images included in an inference data set to detect the object in the unannotated images;
- determine an error rate of the trained CNN model, wherein the error rate is a rate of misdetection of the object in the unannotated images;
- and
- cause the trained CNN model to be further trained with a revised training data set in response to the error rate being greater than a threshold amount.
A method, comprising:
- causing, by a computing device, a convolutional neural network (CNN) model to be trained with an initial training data set to detect an object included in annotated images included in the initial training data set;
- causing, by the computing device, the trained CNN model to perform inferencing on unannotated images included in an inference data set to detect the object in the unannotated images;
- determining, by the computing device, an error rate of the trained CNN model, wherein the error rate is a rate of misdetection of the object in the unannotated images;
- and
- causing, by the computing device, the trained CNN model to be further trained with a revised training data set including annotated images having objects that were mis-detected during the inferencing on the set of unannotated images in response to the error rate being greater than a threshold amount.
Dependencies are inferred from the claim text.