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In the rapid penetration of AI technology, what cannot be ignored is the double guarantee of AI, such as video picture action recognition in hospital CT, as well as various scene recognition in production and life, these changes are triggered by the emergence of pre-trained large model technology.
This emerging technology can be based on zero data, which greatly shortens the training cycle of the model, and provides a solution to the long-term and high-cost problems in the traditional algorithm training mode. In the traditional model, a scenario requires a specific algorithm to collect a large amount of sample data, consuming a lot of time and resources. However, under the wave of digital reform, the emergence of small scenes poses a challenge to this model. In this context, pre-trained large models that can cope with tens of thousands of combinations of scenarios have emerged.
The driving factors behind this technology are the mainland's well-established video big data infrastructure and the rapid increase in the industry's demand for visual recognition. However, only a small percentage of existing smart devices are smart, resulting in a large amount of data that is underutilized. Through the multimodal algorithm of self-supervised learning, the pre-trained large model can process fragmented scenes and fuse information from different modalities, which improves the generalization and generalization ability of the model. This means that a model can cope with a variety of different scenarios, solving the problem of fragmented scene application.
Most excitingly, the pre-trained large model technique not only improves the performance of the model, but also reduces development costs and time. With fewer parameters and data, the recognition accuracy of the model is improved by 40%, the amount of data is reduced by 90%, and the development cycle is shortened from months to days. The landing carrier of this technology, OmVision visual cognition platform, further lowers the threshold of algorithm production, enables front-line personnel who do not understand algorithms to participate in algorithm production, and realizes the close combination of algorithms and applications.
In short, the emergence of pre-trained large model technology marks the arrival of the 20th era of artificial intelligence, which fundamentally changes the production mode of algorithms, realizes that the model is suitable for multiple scenarios, and provides new possibilities for the wide application of AI. The development of this technology will make artificial intelligence easier to use, achieve truly inclusive AI, and promote innovation and development in all walks of life.
OmModel pre-trained large models, as an important representative of this technological revolution, have strong general knowledge ability, not only can recognize a variety of objects, attributes and behaviors, but also have excellent text language understanding ability. This all-around nature enables OmModel to excel in fragmented scenarios across industries.
Using urban management as an example, OmModel can easily identify all kinds of objects on the road, from trees to bicycles to human movements. Even more impressive, the use of OmModel no longer requires the expertise of algorithm engineers. Through text descriptions, any frontline worker can easily define the required algorithm, such as "man on a bicycle" or "trash on the road", and OmModel will quickly generate the corresponding algorithm, which completely breaks the production mode of traditional visual recognition algorithms.
In practical applications, OmModel not only improves the recognition accuracy of the algorithm, but also significantly reduces the amount of training data, saving time and money. The development cycle has been shortened from months to days, and the performance of the algorithm model has become more powerful. This technological change has greatly promoted the development of fragmented application scenarios, allowing all walks of life to fully benefit from the application of artificial intelligence.
At the same time, the emergence of the OmVision visual cognition platform has subverted the traditional algorithm production model. It organically combines the application of algorithms with production, and lowers the professional technical threshold of algorithm production. The platform is able to use the platform to produce and run vision algorithms, and this "algorithm comes from application" philosophy redefines the strategy of algorithm production.
OmVision Studio Algorithm Factory provides low-threshold algorithm production tools, allowing ordinary engineers to train complex algorithms without code, achieving 0-sample cold start and small-sample training. As a vision operating system, OmVision OS realizes personalized online tuning of algorithms and multi-scene visual collaborative prediction, which means that each camera device can have an intelligent brain to achieve high-performance visual recognition.
In summary, the emergence of pre-trained large model technology and the OmVision visual cognition platform not only leads the 20th era of artificial intelligence, but also provides unprecedented opportunities for all walks of life. They not only improve the performance of the model, reduce development costs, but also democratize the production of algorithms. This impetus of technological change will help enable wider AI applications and promote progress and innovation in all areas of society.
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