Target detection model experiment assistant system based on natural language processing

Abstract

The invention belongs to the crossing field of computer vision and natural language processing, and particularly relates to a target detection model experiment assistant system based on natural language processing. Comprising a user interface interaction module, a natural language processing module, a target detection model and data set management module, an experiment process management module and a recommendation system module which are connected in sequence. The recommendation system module is connected with the natural language processing module and transmits a recommendation result of the recommendation system module to the user interface interaction module in a natural language form and displays the recommendation result; and the target detection model and data set management module is connected with the natural language processing module and transmits the target detection model and the data set supported by the target detection model and data set management module to the user interface interaction module in a natural language form for display. According to the system, the user can know the experiment state in real time and make corresponding adjustment, the threshold of research and development of the target detection model is reduced, and more convenient and efficient experiment experience is provided for the user.

Description

Technical Field

[0001] The present invention belongs to the cross - field of computer vision and natural language processing, and particularly relates to an experimental assistant system for object detection models based on natural language processing. Background Art

[0002] In the field of computer vision, object detection, as a core task, has a very complex and time - consuming process for the research and development and optimization of its models. This process usually involves multiple links such as model selection, data preparation, parameter configuration, experiment execution, and result analysis, which have extremely high requirements for the professional skills and practical experience of researchers. Although there are many existing object detection model experimental tools, most of these tools rely on users to operate by modifying code and other means, lacking sufficient flexibility and intelligence, and it is difficult to meet the personalized needs of users.

[0003] Specifically, the existing object detection model experimental tools have the following problems:

[0004] (1) Complicated operation and dependence on manual input: Users need to manually configure model parameters, import data sets, monitor the progress of experiments, etc. The operation process is cumbersome and error - prone. Especially when facing large - scale data sets and complex model structures, this manual operation method is particularly inefficient.

[0005] (2) Lack of intelligence and customization capabilities: Most existing tools provide fixed operation processes and parameter settings, and it is difficult to flexibly adjust according to the actual needs of users. In addition, these tools also lack intelligent recommendation and feedback mechanisms, and cannot provide users with personalized experimental suggestions and optimization plans.

[0006] (3) Insufficient integration of data resources and toolchains: During the research and development process of object detection models, users often need to search for and download relevant data sets and toolchains. However, most existing tools fail to effectively integrate these resources, resulting in users spending a lot of time and effort in the experimental preparation stage. Summary of the Invention

[0007] The present invention solves the problems of poor flexibility, insufficient intelligence and customization services of existing object detection model experimental tools, and provides an experimental assistant system for object detection models based on natural language processing, which realizes seamless communication with users through natural language interaction and automatically executes experimental tasks.

[0008] The technical solutions claimed by the present invention are as follows:

[0009] An experimental assistant system for an object detection model based on natural language processing, comprising a user interface interaction module connected in sequence for receiving user natural language input and user-uploaded projects, a natural language processing module for receiving the natural language input of the user interface interaction module, parsing and identifying the user's experimental requirements, and generating corresponding operation instructions, an object detection model and dataset management module for receiving the corresponding operation instructions generated by the natural language processing module, loading the object detection model, dataset, and corresponding model files and configuration files, an experimental process management module for managing and scheduling the entire experimental process based on the loading of the object detection model and dataset management module and the corresponding operation instructions received thereby, and recording the experimental results, and a recommendation system module for submitting the object detection model, parameters, and dataset based on the experimental results of the experimental process management module; the recommendation system module is connected to the natural language processing module and transmits the recommendation results of the recommendation system module to the user interface interaction module in the form of natural language for display; the object detection model and dataset management module is connected to the natural language processing module and transmits the object detection models and datasets supported by the object detection model and dataset management module to the user interface interaction module in the form of natural language for display for the user to select the supported object detection models and datasets.

[0010] Preferably, the object detection model and dataset management module includes an object detection model sub-module and a dataset management sub-module connected to each other. The object detection model sub-module supports multiple object detection models, and the dataset management sub-module supports datasets in multiple formats; the object detection model management sub-module is responsible for loading, managing, and switching different object detection models; the dataset management sub-module is responsible for loading, preprocessing, and partitioning the dataset; the object detection models include YOLO, SSD, Faster R-CNN, RT-DETR; the datasets include: COCO, VOC, ImageNet.

[0011] Preferably, the object detection model management sub-module also provides target model version management and model performance evaluation functions; the object detection model management sub-module can also recommend relevant object detection project github addresses and dataset download addresses to the user, and the dataset download addresses include download source paths and domestic mirror download paths.

[0012] Preferably, the dataset management sub-module automatically partitions the training set, validation set, and test set according to the user configuration options and the built-in engineering data partitioning method.

[0013] Preferably, the natural language processing module adopts advanced natural language processing technology and incorporates model fine-tuning technology. By training a dedicated dialogue model, it identifies the user's experimental requirements and converts these requirements into operation instructions executable by the system. The natural language processing technology includes word segmentation, text classification, and relation extraction.

[0014] Preferably, the experimental process management module encapsulates the common functions of the target detection model into interfaces, and converts the operation instructions into corresponding parameters and passes them into the interfaces to implement the experimental steps of automatic training, verification, and testing of the model.

[0015] Preferably, the experimental process management module monitors the model performance in real time during the experiment and generates a detailed experimental report after the experiment. The model performance includes: accuracy rate, recall rate, and mAP index.

[0016] Preferably, the experimental process management module is connected to the user interface interaction module and transmits the experimental report to the user interface interaction module.

[0017] Preferably, the recommendation system module recommends suitable target models, parameters, and datasets for the user according to the user's experimental requirements and historical data.

[0018] Preferably, the recommendation system module provides suggestions for model optimization and subsequent research directions for the user according to the user's feedback and experimental results.

[0019] Beneficial effects

[0020] The present invention provides a target detection model experimental assistant system based on natural language processing. The user interface interaction module provides an intuitive and easy-to-use user interaction interface and an intelligent feedback mechanism, enhancing the user's experimental experience and learning efficiency. The natural language processing module parses natural language and generates corresponding system-recognizable operation instructions according to experimental requirements, facilitating subsequent corresponding operations directly based on the execution. The target detection model and dataset management module loads the target detection model, dataset, and corresponding model files and configuration files according to the corresponding operation instructions (operation instructions converted from experimental requirements). The experimental process management module automatically executes experimental operations based on the loading and operation instructions of the target detection model and dataset management module, solving the problem of insufficient intelligence of existing target detection model experimental tools. At the same time, the target detection model and dataset management module supports multiple target models and datasets, and users can conduct customized experiments according to actual needs, meeting different research requirements and solving the problems of insufficient intelligence and customized services of existing target detection model experimental tools. Users input the model to be selected, the dataset to be loaded, and the parameters to be set in the user interface interaction module. The natural language processing module can recognize these experimental requirements and perform experimental operations according to these experimental requirements subsequently, enabling flexible operations according to the user's experimental requirements and solving the problem of poor flexibility of existing target detection model experimental tools.

[0021] In summary, the system of the present invention significantly improves experimental efficiency: through natural language interaction and intelligent management, users can quickly complete experimental settings and operations, saving a large amount of time and energy. It effectively reduces the experimental difficulty: the assistant provides intelligent recommendations and optimization suggestions to help users better select models, parameters, and datasets, lowering the technical threshold of the experiment. It is highly flexible and customizable: the assistant supports multiple models and datasets, and users can conduct customized experiments according to actual needs to meet different research requirements. It enhances the user experience: the intuitive and easy-to-use user interaction interface and intelligent feedback mechanism enhance the user's experimental experience and learning efficiency.

[0022] The target detection model management sub-module can also recommend relevant target detection project github addresses and dataset download addresses to users. The dataset download address includes a download source path and a domestic mirror download path, thus greatly improving the efficiency and convenience of target detection model experiments. Brief Description of the Drawings

[0023] Figure 1 It is a schematic diagram of the target detection model experimental assistant system based on natural language processing according to an embodiment of the present invention. Specific Implementation Method

[0025] To make the objectives, technical solutions, and advantages of the present invention clearer, the following further describes the technical solutions clearly and completely in conjunction with the accompanying drawings of the present invention.

[0026] The present invention provides an experimental assistant system for a target detection model based on natural language processing. As Figure 1 shown, it includes a user interface interaction module that is sequentially connected for receiving user natural language input and user-uploaded projects, a natural language processing module that receives the natural language input of the user interface interaction module, parses and identifies the user's experimental requirements, and generates corresponding operation instructions, a target detection model and dataset management module that receives the corresponding operation instructions generated by the natural language processing module and loads the target detection model, dataset, and corresponding model files and configuration files, an experimental process management module that manages and schedules the entire experimental process based on the loading of the target detection model and dataset management module and the corresponding operation instructions received thereby, and records the experimental results, and a recommendation system module that submits the target detection model, parameters, and dataset based on the experimental results of the experimental process management module; the recommendation system module is connected to the natural language processing module and transmits the recommendation results of the recommendation system module to the user interface interaction module in the form of natural language for display; the target detection model and dataset management module is connected to the natural language processing module and transmits the target detection models and datasets supported by the target detection model and dataset management module to the user interface interaction module in the form of natural language for display for the user to select the supported target detection models and datasets.

[0027] In a specific embodiment of the present invention, the user interaction interface is implemented using Web-based technology, designs a simple and clear interface layout and interaction process, realizes the dynamic display and data interaction of the interface through a front-end framework and a back-end service. During the implementation process, attention is paid to the usability and response speed of the interface, and technologies such as asynchronous loading and lazy loading are adopted to improve the user experience; at the same time, in order to support two modes of text chat and voice interaction, the user interaction interface integrates technologies such as speech recognition and speech synthesis; the user interface interaction module is a window for the experimental assistant system to interact with the user, and the user interface interaction module provides a user interaction interface, and the interface adopts an intuitive and easy-to-use design style and supports text chat. The user can input natural language instructions through the input box for voice input, and the experimental assistant will reply to the user's questions and instructions in text form and provide corresponding operation suggestions and result displays.

[0028] The natural language processing module is the core component of the experimental assistant system, responsible for parsing the user's natural language input, understanding the user's intention, and generating corresponding operation instructions. The natural language processing module adopts a pre-trained language model based on Transformer (such as BERT, GPT, etc.) as the core architecture, and uses advanced natural language processing techniques, such as word segmentation, text classification, relation extraction, etc., to perform pre-training on a large-scale corpus, enabling the model to learn rich language knowledge and context information. For the specific scenario of the object detection model experiment, the technique of model fine-tuning is incorporated to fine-tune the model so that it can better understand the user's natural language instructions and better adapt to the customized needs of different users. During the fine-tuning process, techniques such as transfer learning and multi-task learning are adopted to improve the generalization ability and adaptability of the model. The natural language processing module can accurately identify the user's experimental requirements, such as model selection, dataset loading, parameter setting, etc., by training a dedicated dialogue model, and convert these requirements into operation instructions executable by the system.

[0029] The object detection model and dataset management module includes an interconnected object detection model sub-module and a dataset management sub-module; the object detection model management sub-module is responsible for loading, managing, and switching different object detection models, and the dataset management sub-module is responsible for efficiently loading, preprocessing, and partitioning the dataset. The object detection model management sub-module supports a variety of mainstream object detection models, including but not limited to YOLO, SSD, Faster R-CNN, RT-DETR, etc., and can accurately and automatically load the corresponding model files and configuration files according to the operation instructions. In addition, the object detection model management sub-module also provides functions such as model version management and model performance evaluation, facilitating users to compare and select models. In addition, the dataset management sub-module supports datasets in various formats, such as COCO, VOC, ImageNet, etc., and can perform preprocessing operations such as data augmentation and data normalization according to the operation instructions. At the same time, the dataset management sub-module can also automatically partition the training set, validation set, and test set according to the experimental requirements, ensuring the rationality, effectiveness of the experimental data, and the reliability of the experimental results.

[0030] The experimental process management module is responsible for the management and scheduling of the entire experimental process. The experimental process management module can formulate experimental steps and parameter settings according to the operation instructions, automatically execute experimental steps such as training, validation, and testing, and record the experimental results. During the experiment, techniques such as process control and concurrent processing are used to improve the execution efficiency and stability of the experiment. This module can also monitor the model performance in real time, such as indicators such as accuracy, recall rate, mAP, etc., and generate a detailed experimental report after the experiment. The experimental process management module is connected to the interface interaction module, and transmits the experimental report to the interface interaction module for display, facilitating users to perform result analysis and model optimization.

[0031] The recommendation system module recommends appropriate models, parameters, and datasets for users according to their experimental requirements and historical data. The recommendation system module adopts advanced machine learning algorithms such as collaborative filtering and deep learning, and can continuously learn and optimize the recommendation strategy. By analyzing the users' experimental requirements and historical data, it extracts the users' preferences and features, and recommends appropriate models, parameters, and datasets for users based on these preferences and features to provide more accurate and personalized recommendation services. In the implementation process, technologies such as user portraits and item portraits are adopted to improve the accuracy and personalization degree of recommendations. At the same time, in order to continuously optimize the recommendation strategy, a user feedback mechanism is provided, which can adjust the recommendation results according to the users' feedback. At the same time, the recommendation system module is connected to the natural language processing module, and according to the users' feedback and experimental results, it provides suggestions for model optimization and subsequent research directions, and converts the suggestions into natural language through the natural language processing module and returns them to the user interface interaction module for display.

[0032] To verify the effectiveness and practicality of the present invention, multiple rounds of experimental tests were carried out. The experimental results show that the object detection model experimental assistant system based on natural language processing provided by the present invention can accurately understand the natural language instructions of users and complete corresponding experimental operations; the recommendation system module can provide accurate recommendation services according to the experimental requirements and historical data of users; the user interaction interface module is intuitive and easy to use, and has received unanimous praise from the tested users. Specifically, in experimental links such as model selection, parameter setting, and dataset loading, the accuracy and efficiency of the system provided by the present invention are better than those of traditional GUI tools; in terms of the recommendation system, the recommendation accuracy and user satisfaction also reach a relatively high level; in terms of the user interaction interface, the response speed and ease of use have been widely recognized by users.

[0033] In a specific embodiment of the present invention, the usage steps of the object detection model experimental assistant system based on natural language processing are as follows:

[0034] S1: The user enters the user interaction interface provided by the user interaction interface module, uploads their own code project and dataset (local path or direct upload). If it has been uploaded before, the upload process can be omitted, and then the user's experimental requirements are input in the form of natural language.

[0035] S2: The natural language processing module converts natural language into parameter statements, as shown in Table 1 for example.

[0036] Table 1. Comparison table of user input before and after being processed by the natural language processing module

[0037]

[0038] S3: The target detection model and the dataset management module read the parameter statements of the natural language processing module, update the training parameters to the original parameter file, load the target model and the dataset. Meanwhile, the experimental process management module starts the training script to begin automatic training, and sends the training logs to the natural language processing module to be forwarded into natural language and then transmitted to the user interaction interface module for visual display;

[0039] S4: The recommendation system module will recommend some relatively advanced target models and datasets in the field of target detection to provide users with a rich resource pool;

[0040] S5: After the task is completed, the experimental results are sent to the user interaction interface module and the user is notified.

[0041] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to be limiting. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. An experimental assistant system for an object detection model based on natural language processing, characterized in that, It includes a user interface interaction module for receiving users' natural language input and the projects uploaded by users, which are connected in sequence; a natural language processing module for receiving the natural language input of the user interface interaction module, parsing and identifying the users' experimental requirements, and generating corresponding operation instructions; a target detection model and dataset management module for receiving the corresponding operation instructions generated by the natural language processing module, loading the target detection model, the dataset, and the corresponding model files and configuration files; an experimental process management module for managing and scheduling the entire experimental process based on the loading of the target detection model and dataset management module and the corresponding operation instructions received, and recording the experimental results; and a recommendation system module for submitting the target detection model, parameters, and dataset based on the experimental results of the experimental process management module. The recommendation system module is connected to the natural language processing module and transmits the recommendation results of the recommendation system module to the user interface interaction module in the form of natural language for display. The target detection model and dataset management module is connected to the natural language processing module and transmits the target detection models and datasets supported by the target detection model and dataset management module to the user interface interaction module in the form of natural language for display, so that users can select the supported target detection models and datasets.

2. The experimental assistant system for the object detection model based on natural language processing according to claim 1, wherein The target detection model and dataset management module includes a target detection model sub-module and a dataset management sub-module that are connected to each other. The target detection model sub-module supports multiple target detection models, and the dataset management sub-module supports datasets in multiple formats. The target detection model management sub-module is responsible for loading, managing, and switching different target detection models. The dataset management sub-module is responsible for loading, preprocessing, and partitioning the dataset. The target detection models include YOLO, SSD, Faster R-CNN, and RT-DETR. The datasets include COCO, VOC, and ImageNet.

3. The experimental assistant system for object detection models based on natural language processing according to claim 2, characterized in that, The target detection model management sub-module also provides functions for target model version management and model performance evaluation. The target detection model management sub-module can also recommend relevant target detection project github addresses and dataset download addresses for users. The dataset download addresses include the download source path and the domestic mirror download path.

4. The experimental assistant system for object detection models based on natural language processing according to claim 2, characterized in that, The dataset management sub-module automatically partitions the training set, validation set, and test set according to the built-in engineering data partitioning method based on the user configuration options.

5. The experimental assistant system for object detection models based on natural language processing according to claim 1, characterized in that, The natural language processing module uses advanced natural language processing technologies and incorporates model fine-tuning technologies. By training a dedicated dialogue model, it identifies the users' experimental requirements and converts these requirements into operation instructions that can be executed by the system. The natural language processing technologies include word segmentation, text classification, and relation extraction.

6. The experimental assistant system for object detection models based on natural language processing according to claim 1, wherein The experimental process management module encapsulates the common functions of the target detection model into interfaces, and converts the operation instructions into corresponding parameters and passes them into the interfaces to implement the experimental steps of automatic training, validation, and testing of the model.

7. The experimental assistant system for object detection models based on natural language processing according to claim 6, characterized in that, The experimental process management module monitors the model performance in real time during the experiment and generates a detailed experimental report after the experiment; the model performance includes: accuracy, recall rate, and mAP metric.

8. The experimental assistant system for object detection models based on natural language processing according to claim 7, characterized in that, The experimental process management module is connected to the user interface interaction module and transmits the experimental report to the user interface interaction module.

9. The target detection model experimental assistant system based on natural language processing according to claim 1, characterized in that The recommendation system module recommends appropriate target models, parameters, and datasets for the user according to the user's experimental requirements and historical data.

10. The experimental assistant system for object detection models based on natural language processing according to claim 9, characterized in that, The recommendation system module provides suggestions for model optimization and subsequent research directions for the user based on the user's feedback and experimental results.

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