Vision Transformer application (Joke) In image processing

Currently, The web Ai was created on the BizGPT platform that was able to identify, Analysis and answer images. This chatbot is integrated by the default to the web for free for anyone to own a web set who is trained completely in the role of helping business jobs..

Let's find out about image processing technology behind this wonderful function with BizGPT.

1. About Vision Transformer

Vision Transformer (Joke) is a new breakthrough model in the field of computer vision, Attracting great attention from technology circles thanks to the ability to surpass the neural network (CNN) Tradition in many different tasks. In this article, Vinbigdata will provide in -depth information about Vision Transformer, Including architecture, How to work and their applications in real life.

2. Vision transformer there?

Vision Transformer (Joke) In machine learning is a type of deep learning model designed to process image data with Transformer architecture - which is developed for natural language processing. (NLP). Transformer model, introduced by Vaswani and colleagues in the year 2017, Formed on the self-ATTention mechanism to process input data, Allows it to grasp dependents far away (long-term dependency) and context information more effectively than traditional models such as regression neural network (Rnn). Inspired by the application of Transformer architecture in natural language processing, Scientists have introduced the new transformer architecture for image processing.

3. How does Vision Transformer work??

3.1. Basic knowledge about transformer

To understand Vision Transformer, First of all, it is necessary to master the basic concepts of the Transformer model:

• Self-Attention mechanism: The core of the Transformer model is the self-ATTention mechanism, Help calculate the level of relevance of an input element compared to all other elements. This allows the model to determine the importance of each element based on the context provided by other elements.
• Multi-Head Attention Expand the mechanism of self -attention by applying multiple parallel traffic layers, Allows the model to focus on many different parts of the input at the same time.
• Location encryption (Positional Encoding): Because the transformer architecture does not automatically understand the order of the input elements, So the coding position is added to provide information about the position of each element in the chain.

3.2. Customize transformer in computer vision
While the transformer architecture was originally designed to process sequential data chains, Vision Transformer has adjusted this architecture to process image data by seeing images as a string of small pieces. (patches) With the following way:

• Patch Embedding: The input images are divided into fixed pieces (For example: 16×16 pixel). Each piece is then flattened into a vector and put into space with a higher height.
• Position Embedding: Similar to the position encoding position in NLP, Location encryption is added to each piece to retain space information.
• Transformer encryption kit (Transformer encoder): Series of patch embedding, Together with their Embedding Position, Introduced into a standard transformer encoder, Including multiple layers of the Multi Head Attention mechanism and continued transmission networks.

3. Đout and classify

The output of Transformer encoder is a series of vectors, Each vector corresponds to one piece (patch) of the image. To classify the entire image, A special "class token" is put into the chain, Play a gathering role from all pieces. The final representative of this class token is used for classification tasks.

4. Advantages of Vision Transformer

• HInformation full photo: Vision Transformer (ViT) It is more effective to understand the global context than the neural network (CNN) Thanks to the self-ATTention mechanism, Allows simultaneous processing of all image areas.
• Flexibility: Vision Transformer has a high flexibility, Easy to adapt to many different image resolutions and sizes.
• Expansion: Vision Transformer is able to expand well when increasing model size and training data, Outstanding CNN on large data sets.

5. Application of Vision Transformer

Vision Transformer has shown great potential in many different practical applications, Create practical value in many industries:

4.1. Medical

• Medical image diagnosis: VITS can support analysis of medical images like X-ray, MRI and CT, Help detect abnormalities and diagnose disease with high accuracy.
• Anatomy: VITS can be used to analyze tissue samples, Support cancer detection and other pathologies.

4.2. Self -operating car

• Detect object: VITS enhances the ability to detect and classify objects on the road, Help improve safety and navigation function.
• Recognize the landscape: VITS supports self -propelled self -understanding and analyzing driving environment through panoramic analysis.

4.3. Retail and e -commerce

• Product recognition: VITS can recognize the product in the image, Help manage inventory and automatically pay in the store.
• Proposal personalization: By analyzing image content, They can offer products that are suitable for personal preferences.

4.4. Security and supervision

• Face recognition: VITS improves the accuracy of the face recognition system used in security and supervision.
• Abnormal detection: They can detect abnormal activities or objects in the surveillance scene, Strengthen security measures.

4.5. Environmental monitoring

• Wildlife conservation: VITS helps monitor wildlife and detect poaching behavior through image analysis from camera trap.
• Climate change: VITS supports satellite image analysis to monitor deforestation, Forecast of the melting and environmental changes.

6. The positive effects of Vision Transformer

Vision Transformer has the potential to create significant positive impacts on society:

• Modern advanced medical: Diagnosis early and more accurate, Increase the chance of helping patients and reducing health care costs.
• Traffic is safer: Improve the ability to detect objects and understand the landscape around the self -propelled car can minimize accidents and enhance road traffic safety.
• Enhance retail efficiency: Automation in retail can bring better experience to customers and optimize the process from selecting items to payment.
• Enhance security: More advanced monitoring systems can enhance public security and prevent crimes.
• Bảo vệ môi trường: Better monitoring environmental changes can support conservation and anti -climate change.

Conclude

Vision Transformer represents a breakthrough in the field of computer vision. The ability to process image data with Transformer architecture has opened up new potentials for AI's applications in social life.. We can expect, Vision Transformer will play an increasingly important role in creating a smart world, safe, better, From improving the quality of medical diagnosis, Enhance the capabilities of the car, until environmental protection support.

Nguồn: Medium