基于Vision Transformer的小麦病害图像识别算法

小麦白粉病、赤霉病和锈病是危害小麦产量的三大病害。为提高小麦病害图像的识别准确率,构建一种基于Vision Transformer的小麦病害图像识别算法。首先,通过田间拍摄的方式收集包含小麦白粉病、赤霉病和锈病3种病害在内的小麦病害图像,并对原始图像进行预处理,建立小麦病害图像识别数据集;然后,基于改进的Vision Transformer构建小麦病害图像识别算法,分析不同迁移学习方式和数据增强对模型识别效果的影响。试验可知,全参数迁移学习和数据增强能明显提高Vision Transformer模型的收敛速度和识别精度。最后,在相同时间条件下,对比Vision Transformer、AlexNet和VGG16算法在相同数据集上的表现。试验结果表明,Vision Transformer模型对3种小麦病害图像的平均识别准确率为96.81%,相较于AlexNet和VGG16模型识别准确率分别提高6.68%和4.94%。

细粒度图像分类上Vision Transformer的发展综述

细粒度图像分类(fine-grained image classification,FGIC)一直是计算机视觉领域中的重要问题。与传统图像分类任务相比,FGIC的挑战在于类间对象极其相似,使任务难度进一步增加。随着深度学习的发展,Vision Transformer(ViT)模型在视觉领域掀起热潮,并被引入到FGIC任务中。介绍了FGIC任务所面临的挑战,分析了ViT模型及其特性。主要根据模型结构全面综述了基于ViT的FGIC算法,包括特征提取、特征关系构建、特征注意和特征增强四方面内容,对每种算法进行了总结,并分析了它们的优缺点。通过对不同ViT模型在相同公用数据集上进行模型性能比较,以验证它们在FGIC任务上的有效性。最后指出了目前研究的不足,并提出未来研究方向,以进一步探索ViT在FGIC中的潜力。

Dual-Path Vision Transformer用于急性缺血性脑卒中辅助诊断

急性缺血性脑卒中是由于脑组织血液供应障碍导致的脑功能障碍,数字减影脑血管造影(DSA)是诊断脑血管疾病的金标准。基于患者的正面和侧面DSA图像,对急性缺血性脑卒中的治疗效果进行分级评估,构建基于Vision Transformer的双路径图像分类智能模型DPVF。为了提高辅助诊断速度,基于EdgeViT的轻量化设计思想进行了模型的构建;为了使模型保持轻量化的同时具有较高的精度,提出空间-通道自注意力模块,促进Transformer模型捕获更全面的特征信息,提高模型的表达能力;此外,对于DPVF的两分支的特征融合,构建交叉注意力模块对两分支输出进行交叉融合,促使模型提取更丰富的特征,从而提高模型表现。实验结果显示DPVF在测试集上的准确率达98.5%,满足实际需求。

基于改进Vision Transformer的蝴蝶品种分类

蝴蝶作为一种品类繁多且相似度极高的生物,具有重要的生态环境感知功能。不同品类蝴蝶对环境变化的敏感程度各不相同,因此在农学与生物学研究方向上对蝴蝶的研究具有十分重要的意义。近年来,计算机视觉技术的飞速发展为快速识别蝴蝶品类提供了强有力的技术支持。然而,传统的Vision Transformer模型存在着一些问题,例如缺乏卷积所具有的归纳偏置、局部信息提取能力不足、容易过拟合以及在小数据集上训练缓慢等。针对这些问题,提出了一种基于Vision Transformer改进的蝴蝶分类算法。引入VanillaNet卷积结构,并通过全局注意力机制改进了Class token的更新方式。实验结果显示,在100类蝴蝶数据集上,改进后的Vision Transformer模型的Top-1准确率达到了94.87%,比改进前提升了28.9%。在使用改进的Class token后,算法的Top-1准确率进一步提升至96.64%,相比改进前提升了30.44%。与原网络模型相比,改进后的模型更适用于蝴蝶品种分类任务。

FPGA and computer-vision-based atom tracking technology for scanning probe microscopy

Atom tracking technology enhanced with innovative algorithms has been implemented in this study,utilizing a comprehensive suite of controllers and software independently developed domestically.Leveraging an on-board field-programmable gate array(FPGA)with a core frequency of 100 MHz,our system facilitates reading and writing operations across 16 channels,performing discrete incremental proportional-integral-derivative(PID)calculations within 3.4 microseconds.Building upon this foundation,gradient and extremum algorithms are further integrated,incorporating circular and spiral scanning modes with a horizontal movement accuracy of 0.38 pm.This integration enhances the real-time performance and significantly increases the accuracy of atom tracking.Atom tracking achieves an equivalent precision of at least 142 pm on a highly oriented pyrolytic graphite(HOPG)surface under room temperature atmospheric conditions.Through applying computer vision and image processing algorithms,atom tracking can be used when scanning a large area.The techniques primarily consist of two algorithms:the region of interest(ROI)-based feature matching algorithm,which achieves 97.92%accuracy,and the feature description-based matching algorithm,with an impressive 99.99%accuracy.Both implementation approaches have been tested for scanner drift measurements,and these technologies are scalable and applicable in various domains of scanning probe microscopy with broad application prospects in the field of nanoengineering.

Collaborative positioning for swarms:A brief survey of vision,LiDAR and wireless sensors based methods

As positioning sensors,edge computation power,and communication technologies continue to develop,a moving agent can now sense its surroundings and communicate with other agents.By receiving spatial information from both its environment and other agents,an agent can use various methods and sensor types to localize itself.With its high flexibility and robustness,collaborative positioning has become a widely used method in both military and civilian applications.This paper introduces the basic fundamental concepts and applications of collaborative positioning,and reviews recent progress in the field based on camera,LiDAR(Light Detection and Ranging),wireless sensor,and their integration.The paper compares the current methods with respect to their sensor type,summarizes their main paradigms,and analyzes their evaluation experiments.Finally,the paper discusses the main challenges and open issues that require further research.

Development and validation of a novel questionnaire regarding vision screening among preschool teachers in Malaysia

AIM:To develop and evaluate the validity and reliability of a knowledge,attitude,and practice questionnaire related to vision screening(KAP-VST)among preschool teachers in Malaysia.METHODS:The questionnaire was developed through a literature review and discussions with experts.Content and face validation were conducted by a panel of experts(n=10)and preschool teachers(n=10),respectively.A pilot study was conducted for construct validation(n=161)and test-retest reliability(n=60)of the newly developed questionnaire.RESULTS:Based on the content and face validation,71 items were generated,and 68 items were selected after exploratory factor analysis.The content validity index for items(I-CVI)score ranged from 0.8-1.0,and the content validity index for scale(S-CVI)/Ave was 0.99.Internal consistency was KR^(2)0=0.93 for knowledge,Cronbach’s alpha=0.758 for attitude,and Cronbach’s alpha=0.856 for practice.CONCLUSION:The KAP-VST is a valid and reliable instrument for assessing knowledge,attitude,and practice in relation to vision screening among preschool teachers in Malaysia.

Exploring Deep Learning Methods for Computer Vision Applications across Multiple Sectors:Challenges and Future Trends

Computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other media.Deep learning(DL)methods are more successful than other traditional machine learning(ML)methods inCV.DL techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face recognition.In this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is presented.The sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and CV.This review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

Automatic diagnosis of diabetic retinopathy using vision transformer based on wide-field optical coherence tomography angiography

Diabetic retinopathy(DR)is one of the major causes of visual impairment in adults with diabetes.Optical coherence tomography angiography(OCTA)is nowadays widely used as the golden criterion for diagnosing DR.Recently,wide-field OCTA(WF-OCTA)provided more abundant information including that of the peripheral retinal degenerative changes and it can contribute in accurately diagnosing DR.The need for an automatic DR diagnostic system based on WF-OCTA pictures attracts more and more attention due to the large diabetic population and the prevalence of retinopathy cases.In this study,automatic diagnosis of DR using vision transformer was performed using WF-OCTA images(12 mm×12 mm single-scan)centered on the fovea as the dataset.WF-OCTA images were automatically classified into four classes:No DR,mild nonproliferative diabetic retinopathy(NPDR),moderate to severe NPDR,and proliferative diabetic retinopathy(PDR).The proposed method for detecting DR on the test set achieves accuracy of 99.55%,sensitivity of 99.49%,and specificity of 99.57%.The accuracy of the method for DR staging reaches up to 99.20%,which has been proven to be higher than that attained by classical convolutional neural network models.Results show that the automatic diagnosis of DR based on vision transformer and WF-OCTA pictures is more effective for detecting and staging DR.

Research on intelligent search-and-secure technology in accelerator hazardous areas based on machine vision

Prompt radiation emitted during accelerator operation poses a significant health risk,necessitating a thorough search and securing of hazardous areas prior to initiation.Currently,manual sweep methods are employed.However,the limitations of manual sweeps have become increasingly evident with the implementation of large-scale accelerators.By leveraging advancements in machine vision technology,the automatic identification of stranded personnel in controlled areas through camera imagery presents a viable solution for efficient search and security.Given the criticality of personal safety for stranded individuals,search and security processes must be sufficiently reliable.To ensure comprehensive coverage,180°camera groups were strategically positioned on both sides of the accelerator tunnel to eliminate blind spots within the monitoring range.The YOLOV8 network model was modified to enable the detection of small targets,such as hands and feet,as well as larger targets formed by individuals near the cameras.Furthermore,the system incorporates a pedestrian recognition model that detects human body parts,and an information fusion strategy is used to integrate the detected head,hands,and feet with the identified pedestrians as a cohesive unit.This strategy enhanced the capability of the model to identify pedestrians obstructed by equipment,resulting in a notable improvement in the recall rate.Specifically,recall rates of 0.915 and 0.82were obtained for Datasets 1 and 2,respectively.Although there was a slight decrease in accuracy,it aligned with the intended purpose of the search-and-secure software design.Experimental tests conducted within an accelerator tunnel demonstrated the effectiveness of this approach in achieving reliable recognition outcomes.

Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on Computer Vision Techniques

Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee beans.The deadly disease is hard to control because wind,rain,and insects carry spores.Colombian researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest method.If the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate predictions.To overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate classifications.The proposed methodology selects CBD image datasets through four different stages for training and testing.XGBoost to train a model on datasets of coffee berries,with each image labeled as healthy or diseased.Once themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed model.Some of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions were.Virtual inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of disease.To evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is employed.This involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its performance.In comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.

Clinical usefulness of the baby vision test in young children and its correlation with the Snellen chart

AIM:To investigate the efficacy of a new visual acuity(VA)screening method,the baby vision test for young children.METHODS:A total 105 eyes of 65 children aged 2-8y were included in the study.Acuity testing was conducted using a standardized recognition acuity chart(Snellen visual chart:at 3 m)and the baby vision model assessment.The baby vision device includes a screen,a near infrared camera and a computer.Children were seated at a measured distance of 33-40 cm from a display for testing.VA was estimated according to the highest resolution the children could follow.Decimal VA data were converted to logarithm of the minimum angle of resolution(logMAR)for statistical analysis.The VA results for each child were recorded and analyzed for consistency.RESULTS:The mean VA measured using the Snellen visual chart was 0.62±0.32,and that assessed using the baby vision test was 0.66±0.27.The 95%limit of agreement was-0.609 to 0.695,with 95.2%(100/105)plots within the 95%limits of agreement.VA values of the baby vision test were significantly correlated with those of the Snellen chart(R=0.274,P=0.005).CONCLUSION:The baby vision test can be used as a relatively reliable method for estimating VA in young children.This new acuity assessment might be a valid predictor of optotype-measured acuity later in preverbal children.

Enhancing ChatGPT’s Querying Capability with Voice-Based Interaction and CNN-Based Impair Vision Detection Model

This paper presents an innovative approach to enhance the querying capability of ChatGPT,a conversational artificial intelligence model,by incorporating voice-based interaction and a convolutional neural network(CNN)-based impaired vision detection model.The proposed system aims to improve user experience and accessibility by allowing users to interact with ChatGPT using voice commands.Additionally,a CNN-based model is employed to detect impairments in user vision,enabling the system to adapt its responses and provide appropriate assistance.This research tackles head-on the challenges of user experience and inclusivity in artificial intelligence(AI).It underscores our commitment to overcoming these obstacles,making ChatGPT more accessible and valuable for a broader audience.The integration of voice-based interaction and impaired vision detection represents a novel approach to conversational AI.Notably,this innovation transcends novelty;it carries the potential to profoundly impact the lives of users,particularly those with visual impairments.The modular approach to system design ensures adaptability and scalability,critical for the practical implementation of these advancements.Crucially,the solution places the user at its core.Customizing responses for those with visual impairments demonstrates AI’s potential to not only understand but also accommodate individual needs and preferences.

Frequency and associated factors of accommodation and non-strabismic binocular vision dysfunction among medical university students

AIM:To investigate the frequency and associated factors of accommodation and non-strabismic binocular vision dysfunction among medical university students.METHODS:Totally 158 student volunteers underwent routine vision examination in the optometry clinic of Guangxi Medical University.Their data were used to identify the different types of accommodation and nonstrabismic binocular vision dysfunction and to determine their frequency.Correlation analysis and logistic regression were used to examine the factors associated with these abnormalities.RESULTS:The results showed that 36.71%of the subjects had accommodation and non-strabismic binocular vision issues,with 8.86%being attributed to accommodation dysfunction and 27.85%to binocular abnormalities.Convergence insufficiency(CI)was the most common abnormality,accounting for 13.29%.Those with these abnormalities experienced higher levels of eyestrain(χ2=69.518,P<0.001).The linear correlations were observed between the difference of binocular spherical equivalent(SE)and the index of horizontal esotropia at a distance(r=0.231,P=0.004)and the asthenopia survey scale(ASS)score(r=0.346,P<0.001).Furthermore,the right eye's SE was inversely correlated with the convergence of positive and negative fusion images at close range(r=-0.321,P<0.001),the convergence of negative fusion images at close range(r=-0.294,P<0.001),the vergence facility(VF;r=-0.234,P=0.003),and the set of negative fusion images at far range(r=-0.237,P=0.003).Logistic regression analysis indicated that gender,age,and the difference in right and binocular SE did not influence the emergence of these abnormalities.CONCLUSION:Binocular vision abnormalities are more prevalent than accommodation dysfunction,with CI being the most frequent type.Greater binocular refractive disparity leads to more severe eyestrain symptoms.

A Novel 6G Scalable Blockchain Clustering-Based Computer Vision Character Detection for Mobile Images

6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous Connectivity.In tandem with these advancements,blockchain technology is leveraged to enhance computer vision applications’security,trustworthiness,and transparency.With the widespread use of mobile devices equipped with cameras,the ability to capture and recognize Chinese characters in natural scenes has become increasingly important.Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount,such as facial recognition or personal healthcare monitoring.Users can control their visual data and grant or revoke access as needed.Recognizing Chinese characters from images can provide convenience in various aspects of people’s lives.However,traditional Chinese character text recognition methods often need higher accuracy,leading to recognition failures or incorrect character identification.In contrast,computer vision technologies have significantly improved image recognition accuracy.This paper proposed a Secure end-to-end recognition system(SE2ERS)for Chinese characters in natural scenes based on convolutional neural networks(CNN)using 6G technology.The proposed SE2ERS model uses the Weighted Hyperbolic Curve Cryptograph(WHCC)of the secure data transmission in the 6G network with the blockchain model.The data transmission within the computer vision system,with a 6G gradient directional histogram(GDH),is employed for character estimation.With the deployment of WHCC and GDH in the constructed SE2ERS model,secure communication is achieved for the data transmission with the 6G network.The proposed SE2ERS compares the performance of traditional Chinese text recognition methods and data transmission environment with 6G communication.Experimental results demonstrate that SE2ERS achieves an average recognition accuracy of 88%for simple Chinese characters,compared to 81.2%with traditional methods.For complex Chinese characters,the average recognition accuracy improves to 84.4%with our system,compared to 72.8%with traditional methods.Additionally,deploying the WHCC model improves data security with the increased data encryption rate complexity of∼12&higher than the traditional techniques.

Model Agnostic Meta-Learning(MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating demand.The presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease management.With advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a challenge.This challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease pathogens.In this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data scarcity.This study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural Networks.This paper also proposes a Model AgnosticMeta Learning(MAML)based ensemble classifier for accurate classification.The proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens detection.In future,more diseases can be included for detection along with some other modalities like pests and weed.

Association of age at diagnosis of diabetes with subsequent risk of age-related ocular diseases and vision acuity

BACKGROUND The importance of age on the development of ocular conditions has been reported by numerous studies.Diabetes may have different associations with different stages of ocular conditions,and the duration of diabetes may affect the development of diabetic eye disease.While there is a dose-response relationship between the age at diagnosis of diabetes and the risk of cardiovascular disease and mortality,whether the age at diagnosis of diabetes is associated with incident ocular conditions remains to be explored.It is unclear which types of diabetes are more predictive of ocular conditions.AIM To examine associations between the age of diabetes diagnosis and the incidence of cataract,glaucoma,age-related macular degeneration(AMD),and vision acuity.METHODS Our analysis was using the UK Biobank.The cohort included 8709 diabetic participants and 17418 controls for ocular condition analysis,and 6689 diabetic participants and 13378 controls for vision analysis.Ocular diseases were identified using inpatient records until January 2021.Vision acuity was assessed using a chart.RESULTS During a median follow-up of 11.0 years,3874,665,and 616 new cases of cataract,glaucoma,and AMD,respectively,were identified.A stronger association between diabetes and incident ocular conditions was observed where diabetes was diagnosed at a younger age.Individuals with type 2 diabetes(T2D)diagnosed at<45 years[HR(95%CI):2.71(1.49-4.93)],45-49 years[2.57(1.17-5.65)],50-54 years[1.85(1.13-3.04)],or 50-59 years of age[1.53(1.00-2.34)]had a higher risk of AMD independent of glycated haemoglobin.T2D diagnosed<45 years[HR(95%CI):2.18(1.71-2.79)],45-49 years[1.54(1.19-2.01)],50-54 years[1.60(1.31-1.96)],or 55-59 years of age[1.21(1.02-1.43)]was associated with an increased cataract risk.T2D diagnosed<45 years of age only was associated with an increased risk of glaucoma[HR(95%CI):1.76(1.00-3.12)].HRs(95%CIs)for AMD,cataract,and glaucoma associated with type 1 diabetes(T1D)were 4.12(1.99-8.53),2.95(2.17-4.02),and 2.40(1.09-5.31),respectively.In multivariable-adjusted analysis,individuals with T2D diagnosed<45 years of age[β95%CI:0.025(0.009,0.040)]had a larger increase in LogMAR.Theβ(95%CI)for LogMAR associated with T1D was 0.044(0.014,0.073).CONCLUSION The younger age at the diagnosis of diabetes is associated with a larger relative risk of incident ocular diseases and greater vision loss.

Diagnostic values of questionnaires of Convergence Insufficiency Symptom Survey and College of Optometrists Vision Development Quality of Life in the screening of convergence insufficiency

AIM:To compare and analyse the diagnostic efficacy of the College of Optometrists Vision Development Quality of Life Questionnaire(COVD-QOL)and the Convergence Insufficiency Symptom Survey(CISS)in detecting convergence insufficiency and to compare their diagnostic value in clinical applications.METHODS:Using the diagnostic test method,62 adult patients with convergence insufficiency(age:24.74±3.75y)and 62 normal participants(age:23.61±3.13y)who visited the Optometry Clinic of West China Hospital of Sichuan University from April 2021 to January 2023 were included.All subjects completed the CISS and COVD-QOL.Statistical analysis of the sensitivity and specificity of the CISS and COVD-QOL and comparison and joint experimental analysis of their diagnostic efficacy were performed.RESULTS:The sensitivity of the CISS and COVD-QOL for convergence insufficiency was 64.5%and 71.0%,respectively,while the specificity was 96.8%and 67.7%,respectively.Compared to the CISS alone,the combination of the CISS and COVD-QOL demonstrated lower sensitivity and specificity.The areas under the receiver operating characteristic curve of CISS,COVD-QOL and CISS combined with COVD-QOL were 0.806,0.694 and 0.782,respectively.CONCLUSION:Considering the low sensitivity of the CISS and the low specificity of the COVD-QOL,it is recommended to supplement these questionnaires with other screening tests for the detection of convergence insufficiency.

Posture Detection of Heart Disease Using Multi-Head Attention Vision Hybrid(MHAVH)Model

Cardiovascular disease is the leading cause of death globally.This disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may depend on receiving timely assistance as soon as possible.Thus,minimizing the death ratio can be achieved by early detection of heart attack(HA)symptoms.In the United States alone,an estimated 610,000 people die fromheart attacks each year,accounting for one in every four fatalities.However,by identifying and reporting heart attack symptoms early on,it is possible to reduce damage and save many lives significantly.Our objective is to devise an algorithm aimed at helping individuals,particularly elderly individuals living independently,to safeguard their lives.To address these challenges,we employ deep learning techniques.We have utilized a vision transformer(ViT)to address this problem.However,it has a significant overhead cost due to its memory consumption and computational complexity because of scaling dot-product attention.Also,since transformer performance typically relies on large-scale or adequate data,adapting ViT for smaller datasets is more challenging.In response,we propose a three-in-one steam model,theMulti-Head Attention Vision Hybrid(MHAVH).Thismodel integrates a real-time posture recognition framework to identify chest pain postures indicative of heart attacks using transfer learning techniques,such as ResNet-50 and VGG-16,renowned for their robust feature extraction capabilities.By incorporatingmultiple heads into the vision transformer to generate additional metrics and enhance heart-detection capabilities,we leverage a 2019 posture-based dataset comprising RGB images,a novel creation by the author that marks the first dataset tailored for posture-based heart attack detection.Given the limited online data availability,we segmented this dataset into gender categories(male and female)and conducted testing on both segmented and original datasets.The training accuracy of our model reached an impressive 99.77%.Upon testing,the accuracy for male and female datasets was recorded at 92.87%and 75.47%,respectively.The combined dataset accuracy is 93.96%,showcasing a commendable performance overall.Our proposed approach demonstrates versatility in accommodating small and large datasets,offering promising prospects for real-world applications.

面向Vision Transformer模型的剪枝技术研究

本文针对Vision Transformer(ViT)模型开展剪枝技术研究,探索了多头自注意力机制中的QKV(Query、Key、Value)权重和全连接层(Fully Connected,FC)权重的剪枝问题。针对ViT模型本文提出了3组剪枝方案:只对QKV剪枝、只对FC剪枝以及对QKV和FC同时进行剪枝,以探究不同剪枝策略对ViT模型准确率和模型参数压缩率的影响。本文开展的研究工作为深度学习模型的压缩和优化提供了重要参考,对于实际应用中的模型精简和性能优化具有指导意义。