WiMi Hologram Cloud Inc, a leading global Hologram Augmented Reality (“AR”) Technology provider, announced that a novel image classification system based on a model network of continuous multi-scale feature learning system has been developed which uses well-designed pre-processing and modeling architectures. The model benefits from multi-scale feature extraction and continuous feature learning and achieves better performance in terms of speed and accuracy by using various feature maps with different perceptions as compared to the existing methods.
A continuous multi-scale feature learning system model network of WiMi employs a continuous feature learning approach based on using various feature maps with different receptive fields to achieve faster training/inference and higher accuracy. The system network contains three important steps namely data pre-processing, data learning and inference. In the data pre-processing stage, the dataset images are represented as tensors, which makes the computation during training easier and more efficient. In the data learning phase, useful features of the images are extracted using a model based on continuous multi-scale feature learning. In the inference phase, after completing the second step of the proposed system and obtaining the trained model, the image can be classified using the model.
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In the data pre-processing stage, the dataset images are represented as tensors for subsequent computation and processing. This makes the computation during training easier and more efficient. The process of pre-processing includes normalization, scaling and cropping of the images. The purpose of this step is to make the data more convenient and efficient during the training process and to improve the accuracy and reliability of subsequent processing. This process is to ensure that the input data is processed correctly and can be recognized and learned correctly by the model.
In the data learning phase, this network system of WiMi, uses a continuous multi-scale feature learning method to extract useful features from images. The basic idea of the method is to decompose the image into different scales and then extract the corresponding features at each scale. Image information at different scales contains different feature information, for example, in low-resolution images, the detail information is blurred, but the global information and contour information of the image are still well preserved. Therefore, the robustness and generalization ability of the model can be improved by multi-scale feature extraction.
SOURCE: PRNewswire