YOLOv5 vs YOLOv10: Detailed Model Comparison for Object Detection
Ultralytics YOLO models are at the forefront of real-time object detection, known for their speed and accuracy. This page offers a detailed technical comparison between Ultralytics YOLOv5, a widely-adopted and established model, and YOLOv10, a newer iteration pushing performance boundaries with NMS-free detection. We analyze their architectures, performance benchmarks, and ideal applications to guide users in selecting the right model for their computer vision needs.
YOLOv5: The Proven Industry Standard
Authors: Glenn Jocher
Organization: Ultralytics
Date: 2020-06-26
GitHub Link: https://github.com/ultralytics/yolov5
Docs Link: https://docs.ultralytics.com/models/yolov5/
Ultralytics YOLOv5, released by Ultralytics, quickly became an industry favorite due to its exceptional balance of speed, accuracy, and remarkable ease of use. Built on PyTorch, it's a robust and versatile choice for a broad range of applications, known for its efficient training and straightforward deployment.
Architecture and Key Features
YOLOv5 utilizes a CSPDarknet53 backbone combined with a PANet neck for effective feature aggregation across scales. Its architecture allows for easy scaling, offering various model sizes (n, s, m, l, x) to cater to diverse computational budgets and performance requirements. While anchor-based, which might require some tuning for optimal performance on specific datasets, YOLOv5 benefits immensely from the Well-Maintained Ecosystem provided by Ultralytics. This includes comprehensive documentation, a user-friendly Python package, readily available pre-trained weights for Training Efficiency, and seamless integration with Ultralytics HUB for streamlined workflows from training to deployment. Its Ease of Use and large, active community ensure continuous improvement and readily available support.
Strengths
- Exceptional Inference Speed: Optimized for rapid object detection, making it ideal for real-time systems.
- Scalability and Flexibility: Offers multiple model sizes, allowing users to choose the best Performance Balance between speed and accuracy.
- Ease of Use and Robust Documentation: Known for its simplicity, making it easy to train, validate, and deploy.
- Mature Ecosystem: Benefits from a vast community, extensive resources, and strong Ultralytics support.
- Efficient Training: Requires relatively lower memory compared to transformer-based models and trains quickly.
Weaknesses
- Anchor-Based Detection: Relies on anchor boxes, which may require tuning for optimal performance across diverse datasets.
- Accuracy Trade-off: Smaller models prioritize speed, potentially sacrificing some accuracy compared to larger models or newer architectures like YOLOv10.
Ideal Use Cases
YOLOv5 is widely applicable, especially where speed, reliability, and ease of deployment are critical:
- Edge Computing: Efficient deployment on devices like Raspberry Pi and NVIDIA Jetson due to its speed and smaller model sizes.
- Mobile Applications: Suitable for mobile object detection tasks where computational resources are limited.
- Security and Surveillance: Real-time monitoring and detection in security systems.
- Industrial Automation: Quality control and process automation in manufacturing.
YOLOv10: The Cutting Edge of Real-Time Detection
Authors: Ao Wang, Hui Chen, Lihao Liu, et al.
Organization: Tsinghua University
Date: 2024-05-23
Arxiv Link: https://arxiv.org/abs/2405.14458
GitHub Link: https://github.com/THU-MIG/yolov10
Docs Link: https://docs.ultralytics.com/models/yolov10/
YOLOv10, introduced by researchers from Tsinghua University, represents a significant advancement by focusing on end-to-end real-time object detection without Non-Maximum Suppression (NMS). It aims to enhance efficiency and accuracy through architectural innovations.
Architecture and Features
YOLOv10 introduces consistent dual assignments for NMS-free training, eliminating post-processing bottlenecks and reducing inference latency. It also employs a holistic efficiency-accuracy driven model design strategy, optimizing various components to reduce computational redundancy and enhance model capability. This results in a model that is often faster and more parameter-efficient than previous YOLO versions for similar accuracy levels. YOLOv10 is also integrated into the Ultralytics ecosystem, allowing users to leverage familiar tools and workflows.
Performance Analysis
| Model | size (pixels) |
mAPval 50-95 |
Speed CPU ONNX (ms) |
Speed T4 TensorRT10 (ms) |
params (M) |
FLOPs (B) |
|---|---|---|---|---|---|---|
| YOLOv5n | 640 | 28.0 | 73.6 | 1.12 | 2.6 | 7.7 |
| YOLOv5s | 640 | 37.4 | 120.7 | 1.92 | 9.1 | 24.0 |
| YOLOv5m | 640 | 45.4 | 233.9 | 4.03 | 25.1 | 64.2 |
| YOLOv5l | 640 | 49.0 | 408.4 | 6.61 | 53.2 | 135.0 |
| YOLOv5x | 640 | 50.7 | 763.2 | 11.89 | 97.2 | 246.4 |
| YOLOv10n | 640 | 39.5 | - | 1.56 | 2.3 | 6.7 |
| YOLOv10s | 640 | 46.7 | - | 2.66 | 7.2 | 21.6 |
| YOLOv10m | 640 | 51.3 | - | 5.48 | 15.4 | 59.1 |
| YOLOv10b | 640 | 52.7 | - | 6.54 | 24.4 | 92.0 |
| YOLOv10l | 640 | 53.3 | - | 8.33 | 29.5 | 120.3 |
| YOLOv10x | 640 | 54.4 | - | 12.2 | 56.9 | 160.4 |
Note: YOLOv10 CPU speeds were not reported in the source table.
Strengths
- Superior Speed and Efficiency: Optimized for real-time inference, offering faster processing times and lower latency due to NMS-free design.
- NMS-Free Training/Inference: Simplifies deployment and reduces inference latency.
- High Accuracy with Fewer Parameters: Achieves competitive or superior accuracy with smaller model sizes compared to YOLOv5.
- End-to-End Deployment: Designed for seamless, end-to-end deployment.
Weaknesses
- Newer Model: While integrated by Ultralytics, community resources and third-party tools might still be developing compared to the highly established YOLOv5.
- Optimization Complexity: Achieving peak performance might require fine-tuning specific to hardware and datasets.
Ideal Use Cases
YOLOv10 is ideally suited for applications demanding ultra-fast and efficient object detection with minimal latency:
- High-Speed Robotics: Enabling robots to process visual data in real-time for dynamic environments.
- Advanced Driver-Assistance Systems (ADAS): Providing rapid and accurate object detection for enhanced road safety.
- Real-Time Video Analytics: Processing high-frame-rate video streams for immediate insights.
Conclusion
Both Ultralytics YOLOv5 and YOLOv10 are powerful object detection models. YOLOv5 remains an excellent choice due to its maturity, extensive support, ease of use, and proven reliability across a vast range of applications, especially benefiting from the robust Ultralytics ecosystem. YOLOv10 offers cutting-edge performance, particularly in latency-critical scenarios, thanks to its NMS-free architecture and efficiency optimizations. While newer, its integration into the Ultralytics framework makes it accessible. The choice depends on project priorities: YOLOv5 for a proven, versatile, and easy-to-use solution with strong community backing, or YOLOv10 for state-of-the-art speed and end-to-end efficiency.
For further exploration, consider comparing these models with other state-of-the-art architectures available within the Ultralytics ecosystem, such as YOLOv8, YOLOv9, and the latest YOLO11. You can also find comparisons against models like RT-DETR in the comparison section.
