mlsanityData to 7 Oct 2026

Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithm

arXiv 2410.12259 ↗First posted 16 Oct 2024cs.CV cs.LGcited by 11

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Abstract (arXiv)

This paper explores the application of knowledge distillation technology in target detection tasks, especially the impact of different distillation temperatures on the performance of student models. By using YOLOv5l as the teacher network and a smaller YOLOv5s as the student network, we found that with the increase of distillation temperature, the student's detection accuracy gradually improved, and finally achieved mAP50 and mAP50-95 indicators that were better than the original YOLOv5s model at a specific temperature. Experimental results show that appropriate knowledge distillation strategies can not only improve the accuracy of the model but also help improve the reliability and stability of the model in practical applications. This paper also records in detail the accuracy curve and loss function descent curve during the model training process and shows that the model converges to a stable state after 150 training cycles. These findings provide a theoretical basis and technical reference for further optimizing target detection algorithms.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 12 of its 22 references that are in the map. First posted on arXiv 16 Oct 2024.

Builds on

12 of 22 references.

References · 12

  1. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
  2. 2024Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Miningcited by 47
  3. 2023Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNNcited by 35
  4. 2024Breast Cancer Image Classification Method Based on Deep Transfer Learningcited by 12
  5. 2024Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessmentcited by 11
  6. 2024Research on Image Super-Resolution Reconstruction Mechanism based on Convolutional Neural Networkcited by 8
  7. 2024Contrastive Learning for Knowledge-Based Question Generation in Large Language Modelscited by 8
  8. 2024Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysiscited by 8
  9. 2024Convolutional Neural Networks for Predictive Modeling of Lung Diseasecited by 7
  10. 2024Reducing Bias in Deep Learning Optimization: The RSGDM Approachcited by 7
  11. 2024Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processingcited by 7
  12. 2024Graph Neural Network Framework for Sentiment Analysis Using Syntactic Featurecited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 8; 2025: 2; 2026: 1.

11 papers.

  1. 2024Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generationcited by 13
  2. 2024Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learningcited by 13
  3. 2024Metric Learning for Tag Recommendation: Tackling Data Sparsity and Cold Start Issuescited by 12
  4. 2024Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classificationcited by 11
  5. 2024Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systemscited by 9
  6. 2024Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environmentscited by 7
  7. 2024A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generationcited by 7
  8. 2025Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Modelscited by 6
  9. 2024Leveraging Generative Adversarial Networks for Addressing Data Imbalance in Financial Market Supervisioncited by 5
  10. 2025Architectural Insights into Knowledge Distillation for Object Detection: A Comprehensive Reviewcited by 1
  11. 2026DLRMamba: Distilling Low-Rank Mamba for Edge Multispectral Fusion Object Detectioncited by 1