Image recognition model training method, recognition method, device, medium and equipment
An image recognition and training method technology, applied in the field of image processing, can solve problems such as overfitting, ignoring model generalization problems, and complex model processes, so as to improve recognition accuracy, avoid overfitting problems, and ensure accuracy. Effect
Active Publication Date: 2022-07-29
BEIJING BYTEDANCE NETWORK TECH CO LTD
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However, the methods in the related art ignore the generalization problem of the model on the new center, and do not pay attention to the additional knowledge in the multi-center training data
This will lead to the need to collect data from the new center to fine-tune the trained model every time the model is deployed to a new center to ensure the generalization performance of the model, otherwise it will affect the accuracy of the model for endoscopic image recognition
Moreover, the process of fine-tuning the trained model is complicated every time the model is deployed, and it may cause problems such as overfitting, which will affect the recognition accuracy of the model
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[0039] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for the purpose of A more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes, and are not intended to limit the protection scope of the present disclosure.
[0040] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. Furthermore, method embodiments may include additional steps and / or omit performing the illustrated steps. The scope of the present discl...
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The present disclosure relates to an image recognition model training method, recognition method, device, medium and device. The method includes: acquiring multiple training sample sets, the data distribution of each training sample set is not completely consistent, and for each training image, Determine the gradient of the training image according to the training image and the training recognition result corresponding to the training image, determine the first statistic and the second statistic of each training sample set according to the gradient of each training image, and determine the first statistic and the second statistic of each training sample set according to the gradient of each training image. and the second statistic, determine the statistic loss function, update the preset model according to the statistic loss function, and obtain an image recognition model. The present disclosure can update the preset model according to the statistic loss function determined by the first statistic and the second statistic to obtain an image recognition model with high generalization performance, and does not need to perform additional fine-tuning on the image recognition model, which can avoid causing The overfitting problem improves the recognition accuracy of the image recognition model.
Description
technical field [0001] The present disclosure relates to the technical field of image processing, and in particular, to an image recognition model training method, recognition method, apparatus, medium and device. Background technique [0002] Colorectal cancer is one of the malignant tumors with the highest incidence in my country, but early diagnosis and appropriate treatment of cancer can bring about a 90% cure rate. Regular colonoscopy screening can identify adenomatous polyps and prevent cancer. During endoscopy, it is critical to identify the ileocecal region in endoscopic images. [0003] Currently, endoscopic image recognition is mainly based on deep neural networks (eg, convolutional neural networks). In order to achieve good generalization performance, a large amount of training data needs to be collected for training. The training data may come from the same medical center or from different medical centers. However, the methods in the related art ignore the gen...
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IPC IPC(8): G06V10/774G06K9/62
CPCG06F18/214
Inventor 边成李永会杨延展
Owner BEIJING BYTEDANCE NETWORK TECH CO LTD