Model training and image processing method and device, equipment and storage medium
A model training and model technology, applied in the field of artificial intelligence, can solve problems such as inability to apply multi-target image model training, poor versatility, etc., to achieve the effect of reducing high requirements and high dependencies, good versatility, and improved performance
Pending Publication Date: 2022-04-29
SHANGHAI SENSETIME INTELLIGENT TECH CO LTD +1
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[0003] However, the self-supervised training algorithm in the related art can only perform model training on a single target image, but cannot be applied to model training on multi-target images, and has poor versatility
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[0038] In order to make the purpose, technical solution and advantages of the application more clear, the technical solution of the application will be further elaborated below in conjunction with the accompanying drawings and embodiments. The described embodiments should not be considered as limiting the application. All other embodiments obtained under the premise of no creative work belong to the scope of protection of this application.
[0039] In the following description, references to "some embodiments" describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or a different subset of all possible embodiments, and Can be combined with each other without conflict. The term "first / second / third" involved is only to distinguish similar objects, and does not represent a specific ordering for the objects. It is understandable that "first / second / third" can be used interchangeably when permitted. The specific order or se...
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The embodiment of the invention discloses a model training and image processing method and device, equipment, a storage medium and a computer program product, and the method comprises the steps: determining a first scene image and a second scene image based on a first image sample; an overlapping region exists between the first scene image and the second scene image; respectively carrying out feature extraction on the first scene image and the second scene image by utilizing a to-be-trained first model to obtain a first scene feature of the first scene image and a second scene feature of the second scene image, and respectively carrying out feature extraction on the first scene image and the second scene image by utilizing a second model to obtain a first scene feature of the first scene image and a second scene feature of the second scene image; obtaining a third scene feature of the first scene image and a fourth scene feature of the second scene image; determining a target loss value based on the first scene feature, the second scene feature, the third scene feature and the fourth scene feature; and based on the target loss value, updating the model parameters of the first model at least once to obtain the trained first model.
Description
technical field [0001] The present application relates to but not limited to the field of artificial intelligence, and in particular relates to a model training and image processing method, device, equipment, storage medium and computer program product. Background technique [0002] With the continuous development of computer vision technology, the acquisition of unlabeled data is becoming easier and easier. However, for the massive unlabeled data sets in the field of computer vision, using manual methods for labeling will have the problem of missing labels and consume a lot of labor costs. In related technologies, a self-supervised training algorithm may be used to train the neural network model. Self-supervised training algorithms can train models without providing labeled data, and provide pre-trained models for various tasks in the field of computer vision. Compared with supervised training algorithms, self-supervised training algorithms have obvious advantages in redu...
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IPC IPC(8): G06V10/764G06V10/40G06V10/26G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/24
Inventor 李朝闻朱优松杨帆李韡赵朝阳陈志扬吴立威赵瑞唐明王金桥
Owner SHANGHAI SENSETIME INTELLIGENT TECH CO LTD