Skip to content

YOLOv7 vs YOLOv5: A Detailed Comparison

Ultralytics YOLO models are renowned for their speed and accuracy in object detection tasks. This page provides a technical comparison between two popular versions: YOLOv7 and YOLOv5, highlighting their architectural differences, performance metrics, and suitable use cases.

Both models are built upon the YOLO (You Only Look Once) framework, but they incorporate distinct innovations that cater to different needs in the computer vision landscape. Choosing between YOLOv7 and YOLOv5 depends on the specific requirements of your project, such as the balance between speed and accuracy, computational resources, and deployment environment.

YOLOv5: Speed and Efficiency

Ultralytics YOLOv5 is characterized by its highly modular architecture and ease of use. It offers a range of model sizes, from YOLOv5n (nano) to YOLOv5x (extra large), allowing users to select a model that best fits their speed and accuracy requirements. YOLOv5 is implemented in PyTorch and is known for its fast inference speed, making it ideal for real-time applications and deployment on edge devices. Its architecture is designed for efficient computation, leveraging techniques like CSPBottleneck and focus layers to optimize performance.

YOLOv5 excels in scenarios where speed is paramount, such as real-time object detection in video streams, robotics, and applications with limited computational resources. It is also a popular choice for rapid prototyping and deployment due to its user-friendly interface and comprehensive documentation. For detailed information, refer to the Ultralytics YOLOv5 documentation and the Ultralytics GitHub repository.

Learn more about YOLOv5

YOLOv7: Enhanced Accuracy and Efficiency

Ultralytics YOLOv7 builds upon the foundation of previous YOLO versions, introducing architectural improvements focused on increasing both accuracy and efficiency. YOLOv7 incorporates techniques like Extended Efficient Layer Aggregation Networks (E-ELAN) and model re-parameterization to enhance learning and inference capabilities. While maintaining real-time performance, YOLOv7 generally achieves higher mAP (mean Average Precision) compared to YOLOv5, especially in more complex object detection tasks.

YOLOv7 is well-suited for applications demanding higher accuracy without significant sacrifices in speed. This includes scenarios like detailed image analysis, high-precision object detection in manufacturing quality control, and advanced video analytics. It offers a balance between computational efficiency and detection precision, making it a robust choice for a wide range of computer vision tasks. Explore the Ultralytics YOLOv7 documentation for in-depth technical details and usage guidelines.

Learn more about YOLOv7

Performance Metrics Comparison

The table below summarizes the performance metrics for YOLOv7 and YOLOv5 models on the COCO dataset. Note that performance can vary based on hardware, software, and specific implementation details. For detailed performance benchmarks and comparisons, refer to the official Ultralytics documentation and benchmark scripts.

Model size
(pixels)
mAPval
50-95
Speed
CPU ONNX
(ms)
Speed
T4 TensorRT10
(ms)
params
(M)
FLOPs
(B)
YOLOv7l 640 51.4 - 6.84 36.9 104.7
YOLOv7x 640 53.1 - 11.57 71.3 189.9
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

Choosing Between YOLOv7 and YOLOv5

  • Choose YOLOv5 if: Your primary concern is inference speed and efficiency, especially for real-time applications on edge devices or resource-constrained environments. YOLOv5's different size variants offer flexibility to tailor the model to specific hardware limitations.
  • Choose YOLOv7 if: Higher accuracy is crucial for your application, and you have sufficient computational resources to support a slightly more complex model. YOLOv7 provides improved detection precision while maintaining competitive inference speeds.

Both models are powerful tools within the Ultralytics YOLO ecosystem. Your choice should be driven by the specific trade-offs between speed, accuracy, and resource availability relevant to your computer vision project.

Further Exploration

Interested in exploring other models? Ultralytics offers a range of cutting-edge YOLO models, including:

  • YOLOv8: The latest iteration, offering state-of-the-art performance and versatility. Learn more about YOLOv8.
  • YOLOv9: Introducing further advancements in efficiency and accuracy for real-time object detection. Explore YOLOv9 documentation.
  • YOLOv10: The newest model in the YOLO series, pushing the boundaries of real-time object detection without NMS. Discover YOLOv10.
  • YOLO-NAS: A Neural Architecture Search (NAS) derived model focusing on optimized performance and quantization support. Explore YOLO-NAS.
  • YOLOv6: Explore Meituan's YOLOv6 for a balance of speed and accuracy. Learn about YOLOv6.
  • YOLOv4: Discover Alexey Bochkovskiy's YOLOv4, a highly influential real-time object detector. Explore YOLOv4 documentation.
  • YOLOv3: Understand the architecture and features of YOLOv3 and its variants. Learn about YOLOv3.
  • YOLOv11: The groundbreaking model redefining computer vision with unmatched accuracy and efficiency. Discover YOLOv11.

Explore the Ultralytics Docs for a comprehensive overview of all available models and their capabilities. You can also engage with the community and explore practical guides in the Ultralytics Guides section.

📅 Created 1 year ago ✏️ Updated 1 month ago

Comments