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Micro-beauty holography (NASDAQ:WIMI) constructs an image classification and fusion model based on depth transfer learning to improve the accuracy and efficiency of image classification.

Deep learning is more and more widely used in the field of computer vision, especially in image classification tasks. However, due to the limitation of the data set and the complexity of the model, the performance of the deep learning model on the small sample data set still needs to be improved.

In order to solve this problem, micro-beauty holography (NASDAQ:WIMI) introduces transfer learning into the task of image classification, constructs an image classification fusion model, and improves the classification performance on small sample data sets by using the feature representation of the model trained on large-scale data sets.

Deep transfer learning can apply deep learning models that have been trained on large-scale data sets to new tasks. In image classification, deep transfer learning can accelerate the training process of the model and improve the classification performance by transferring some or all of the network parameters of the trained model to the new model. The image features are extracted by the pre-trained depth neural network model, and the classifier model is used for classification, and the pre-trained depth neural network model is connected with the classifier model. Finally, the whole model is optimized by end-to-end training and back propagation algorithm. This method can effectively use the feature knowledge that has been learned and improve the accuracy and efficiency of image classification.

In the image classification fusion model based on deep transfer learning constructed by WIMI micro-beauty holography, it adopts a design and implementation method of fusion model, which combines several pre-trained deep learning models and fuses them together by means of transfer learning to improve the accuracy and robustness of image classification. The model architecture mainly includes the following key components:

Basic model selection: in the design of fusion model, we first need to select some basic deep learning models as candidate models. These models are pre-trained on large-scale image data sets, and they have good performance and wide application in image classification tasks.

Feature extraction layer: in order to integrate different basic models, a feature extraction layer needs to be added to each model. The function of this feature extraction layer is to convert the input image into a high-dimensional feature vector so that the subsequent classifier can classify it. In this feature extraction layer, convolution neural network (CNN) is used to realize feature extraction.

Fusion layer: after the feature extraction layer, the feature vectors extracted by multiple basic models will be obtained. In order to fuse them together, and then design a fusion layer, the purpose of the fusion layer is to fuse multiple feature vectors into a more expressive feature vector, in order to improve the effect of classification.

Classifier: after the fusion layer, a fused feature vector will be obtained. In order to carry out the final classification, we will need to add a classifier, through the classifier to map the fused feature vectors to different categories, so as to achieve image classification.

Combining the advantages of multiple basic models can improve the accuracy and robustness of image classification. At the same time, the image classification and fusion model based on deep transfer learning also has some flexibility, and different basic models and fusion methods can be selected according to the actual situation to adapt to different image classification tasks.

Image recognition is an important application scene of deep learning in the field of computer vision. The image classification and fusion model based on deep transfer learning studied by WIMI micro-beauty holography will also be widely used in more industry fields. For example, in the field of intelligent security, the model can be used for real-time face recognition of the images captured by the surveillance camera, so as to realize the automatic alarm to strangers. Autopilot is also another important application scenario. Through the image classification and fusion model of deep transfer learning, we can realize the recognition and classification of traffic signs, vehicles and pedestrians on the road. This is very important for self-driving vehicles, which can help vehicles judge the changes in the surrounding environment and make corresponding decisions. For example, when a vehicle recognizes that a pedestrian is crossing the road, braking measures can be taken in time to ensure the safety of pedestrians. In addition, the model can also be used in the automatic parking system of vehicles, which can realize the automatic parking of vehicles by identifying parking spaces and obstacles. In addition, social media analysis is also an application scenario that uses the image classification and fusion model of deep transfer learning to analyze and classify images on social media. By classifying the images on social media, we can understand the interests and preferences of users. For example, by analyzing the photos posted by users on social media, you can recommend related products or activities and provide personalized recommendation services. In addition, social media analysis can also be used for emotional analysis, by identifying the expressions and emotions in the image, to understand the emotional state of users, so as to provide better services and marketing strategies for enterprises.

In addition to the above application scenarios, the image classification and fusion model based on deep transfer learning can also be applied to many other fields, such as smart home, intelligent manufacturing, intelligent assistant and so on. Through the recognition and classification of images, we can achieve intelligent perception and understanding of the environment and objects, and bring more convenience and efficiency to people's life and work.

With the successful application of deep transfer learning in image classification tasks, WIMI micro-beauty holography will pay more attention to explore and improve the image classification and fusion model based on deep transfer learning from the aspects of cross-domain transfer learning, model interpretation and small sample learning in order to further improve the performance and application range of image classification tasks.

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