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YOLOv5 vs RT-DETR v2: A Detailed Model Comparison

Choosing the right object detection model is crucial for computer vision projects. Ultralytics YOLO offers a suite of models tailored for various needs. This page provides a technical comparison between Ultralytics YOLOv5 and RT-DETR v2, highlighting their architectural differences, performance metrics, and ideal applications.

YOLOv5: Speed and Efficiency

Ultralytics YOLOv5 is a highly popular one-stage object detector known for its speed and efficiency. Its architecture is based on:

  • Backbone: CSPDarknet53 for feature extraction.
  • Neck: PANet for feature fusion.
  • Head: YOLOv5 head for detection.

YOLOv5 comes in various sizes (n, s, m, l, x), offering a trade-off between speed and accuracy.

Strengths:

  • Speed: YOLOv5 excels in inference speed, making it suitable for real-time applications.
  • Efficiency: Models are relatively small and require less computational resources.
  • Versatility: Adaptable to various hardware, including edge devices.
  • Ease of Use: Well-documented and easy to implement with Ultralytics Python package and Ultralytics HUB.

Weaknesses:

  • Accuracy: While highly accurate, larger models like RT-DETR v2 may achieve higher mAP, especially on complex datasets.

Use Cases:

Learn more about YOLOv5

RT-DETR v2: Accuracy with Transformer Efficiency

RT-DETR v2 represents a shift towards Transformer-based architectures for real-time object detection. It leverages:

  • Backbone: Hybrid backbone combining CNNs and Transformers for efficient feature extraction.
  • Decoder: Transformer decoder inspired by DETR (DEtection TRansformer) for direct set prediction, eliminating the need for Non-Maximum Suppression (NMS) in the model architecture.

RT-DETR v2 also offers different sizes (s, m, l, x) to balance accuracy and speed.

Strengths:

  • Accuracy: RT-DETR v2 achieves state-of-the-art accuracy, particularly the larger models, due to its transformer-based architecture which excels at capturing global context.
  • Robustness: DETR-style models are known for their robustness and ability to handle complex scenes.
  • NMS-free: Simplifies the pipeline and potentially improves latency by removing the NMS post-processing step from the model itself.

Weaknesses:

  • Speed: While optimized for real-time, RT-DETR v2 may be slightly slower in inference speed compared to smaller YOLOv5 models, especially on CPU.
  • Model Size: Transformer-based models can be larger than traditional CNN-based models.

Use Cases:

  • Applications prioritizing high accuracy object detection.
  • Complex scene understanding and detailed image analysis.
  • Scenarios where robustness to occlusion and cluttered backgrounds is important.
  • Industrial inspection and quality control (AI in Manufacturing).
  • Medical image analysis (medical image analysis).

Learn more about RT-DETR v2

Model Comparison Table

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
RTDETRv2-s 640 48.1 - 5.03 20 60
RTDETRv2-m 640 51.9 - 7.51 36 100
RTDETRv2-l 640 53.4 - 9.76 42 136
RTDETRv2-x 640 54.3 - 15.03 76 259

Conclusion

Both YOLOv5 and RT-DETR v2 are powerful object detection models, each with its strengths. YOLOv5 is ideal when speed and efficiency are paramount, while RT-DETR v2 shines in scenarios demanding the highest accuracy. The choice between them depends on the specific requirements of your project.

Users might also be interested in exploring other Ultralytics YOLO models such as YOLOv8, YOLOv10, YOLO-NAS, YOLOv7, YOLOv9 and YOLOv6 for different performance characteristics and architectural innovations.

For further details, refer to the official Ultralytics Documentation and the Ultralytics GitHub repository.

📅 Created 1 year ago ✏️ Updated 1 month ago

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