BEIJING, Nov. 27,
2023 /PRNewswire/ -- WiMi Hologram Cloud Inc.
(NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram
Augmented Reality ("AR") Technology provider, today announced that
to fully utilize the advantages of deep learning models and machine
learning models, an image classification system that integrates
deep learning and machine learning models was developed. The system
first uses a deep learning model for feature extraction to
transform the original image data into high-level represented
features. Then, machine learning models are used to classify these
high-level features to obtain the final image classification
results. Specifically, WiMi uses a convolutional neural network
(CNN) as a deep learning model to extract local features of an
image through multiple convolutional and pooling layers, and
combines these features through a fully connected layer to obtain a
high-level representation of the image. A support vector machine
(SVM) is then used as the machine learning model to input these
high-level features into the SVM for classification.
SVM is a classical machine learning algorithm that can classify
feature vectors based on their linear divisibility. By fusing deep
learning and machine learning models, the feature extraction
ability of the deep learning model and the classification ability
of the machine learning model are fully utilized to improve the
accuracy of image classification. Meanwhile, due to the separation
of the deep learning model and the machine learning model, either
module can be flexibly adapted and replaced for different image
classification tasks and datasets to achieve more accurate and
efficient image classification tasks.
The deep learning model can learn more abstract and advanced
features, while the machine learning model can utilize these
features for more accurate classification. WiMi improves the
accuracy of image classification by fusing the deep learning and
machine learning models, utilizing the powerful feature extraction
capability of the deep learning model and the excellent
classification capability of the machine learning model, and, at
the same time, optimizes the computational process of image
classification to improve the efficiency of classification. Deep
learning models usually require a large amount of computational
resources for training and inference, while machine learning models
are relatively lightweight and can perform classification with
lower computational resources. Deep learning and machine learning
models are fused to build an end-to-end image classification
system. The system can receive input images and output
classification results. Users can classify images and get the
classification results through this system.
The whole system depends on the synergy of all the aspects such
as data pre-processing, feature extraction, feature fusion,
advanced feature extraction and classifier training
Data pre-processing: Pre-processing the input image data,
including image resizing, normalization and other operations, to
facilitate subsequent model training and classification.
Deep learning model training: Such as CNN training to learn the
high-level feature of the image. We will use existing deep learning
model architectures and train on large-scale image datasets to
improve the generalization ability of the model.
Machine learning model training: Such as SVM training utilises
the features extracted by the deep learning model for
classification. We will use the middle layer output of the deep
learning model as the input features of the machine learning model
and train and optimize it by tuning the model parameters.
Model fusion: Deep learning models and machine learning models
will be fused to build a comprehensive image classification system.
We will get the final classification results by weighted fusion or
integrated learning of the classification results of the two
models.
Through the above steps, we will realize an image classification
system that fuses deep learning and machine learning models to
improve classification accuracy and efficiency, and provide users
with an end-to-end image classification solution.
WiMi's image classification system based on deep learning and
machine learning models has a wide range of practical application
scenarios, and it has a wide range of application prospects in
practical application scenarios such as healthcare, intelligent
transportation, security monitoring, and autonomous driving. For
example, in the field of intelligent transportation, the image
classification system integrating deep learning and machine
learning models can detect and identify vehicle types, license
plate numbers, and traffic signs in the traffic scene in real-time,
thus providing real-time traffic information and intelligent
traffic management. In the field of autonomous driving, the system
can detect and recognize lane lines, traffic signs, and obstacles
on the road in real-time, to realize precise control and safe
driving of autonomous vehicles.
In the future, WiMi will continue to optimize the image
classification system by increasing the dataset, optimizing the
network structure, introducing the attention mechanism, combining
multiple models, and using migration learning to further improve
the system performance and expand its applications in more
industries.
About WIMI Hologram Cloud
WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud
comprehensive technical solution provider that focuses on
professional areas including holographic AR automotive HUD
software, 3D holographic pulse LiDAR, head-mounted light field
holographic equipment, holographic semiconductor, holographic cloud
software, holographic car navigation and others. Its services and
holographic AR technologies include holographic AR automotive
application, 3D holographic pulse LiDAR technology, holographic
vision semiconductor technology, holographic software development,
holographic AR advertising technology, holographic AR entertainment
technology, holographic ARSDK payment, interactive holographic
communication and other holographic AR technologies.
Safe Harbor Statements
This press release contains "forward-looking statements" within the
Private Securities Litigation Reform Act of 1995. These
forward-looking statements can be identified by terminology such as
"will," "expects," "anticipates," "future," "intends," "plans,"
"believes," "estimates," and similar statements. Statements that
are not historical facts, including statements about the Company's
beliefs and expectations, are forward-looking statements. Among
other things, the business outlook and quotations from management
in this press release and the Company's strategic and operational
plans contain forward−looking statements. The Company may also make
written or oral forward−looking statements in its periodic reports
to the US Securities and Exchange Commission ("SEC") on Forms 20−F
and 6−K, in its annual report to shareholders, in press releases,
and other written materials, and in oral statements made by its
officers, directors or employees to third parties. Forward-looking
statements involve inherent risks and uncertainties. Several
factors could cause actual results to differ materially from those
contained in any forward−looking statement, including but not
limited to the following: the Company's goals and strategies; the
Company's future business development, financial condition, and
results of operations; the expected growth of the AR holographic
industry; and the Company's expectations regarding demand for and
market acceptance of its products and services.
Further information regarding these and other risks is included
in the Company's annual report on Form 20-F and the current report
on Form 6-K and other documents filed with the SEC. All information
provided in this press release is as of the date of this press
release. The Company does not undertake any obligation to update
any forward-looking statement except as required under applicable
laws.
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SOURCE WiMi Hologram Cloud Inc.