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ultralytics-ascend/docs/en/models/yolov5.md
Glenn Jocher b06c5a4b9e
Add Docs models pages FAQs (#14167) 
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
2024-07-02 20:03:58 +02:00

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true Explore YOLOv5u, an advanced object detection model with optimized accuracy-speed tradeoff, featuring anchor-free Ultralytics head and various pre-trained models. YOLOv5, YOLOv5u, object detection, Ultralytics, anchor-free, pre-trained models, accuracy, speed, real-time detection

YOLOv5

Overview

YOLOv5u represents an advancement in object detection methodologies. Originating from the foundational architecture of the YOLOv5 model developed by Ultralytics, YOLOv5u integrates the anchor-free, objectness-free split head, a feature previously introduced in the YOLOv8 models. This adaptation refines the model's architecture, leading to an improved accuracy-speed tradeoff in object detection tasks. Given the empirical results and its derived features, YOLOv5u provides an efficient alternative for those seeking robust solutions in both research and practical applications.

Ultralytics YOLOv5

Key Features

  • Anchor-free Split Ultralytics Head: Traditional object detection models rely on predefined anchor boxes to predict object locations. However, YOLOv5u modernizes this approach. By adopting an anchor-free split Ultralytics head, it ensures a more flexible and adaptive detection mechanism, consequently enhancing the performance in diverse scenarios.

  • Optimized Accuracy-Speed Tradeoff: Speed and accuracy often pull in opposite directions. But YOLOv5u challenges this tradeoff. It offers a calibrated balance, ensuring real-time detections without compromising on accuracy. This feature is particularly invaluable for applications that demand swift responses, such as autonomous vehicles, robotics, and real-time video analytics.

  • Variety of Pre-trained Models: Understanding that different tasks require different toolsets, YOLOv5u provides a plethora of pre-trained models. Whether you're focusing on Inference, Validation, or Training, there's a tailor-made model awaiting you. This variety ensures you're not just using a one-size-fits-all solution, but a model specifically fine-tuned for your unique challenge.

Supported Tasks and Modes

The YOLOv5u models, with various pre-trained weights, excel in Object Detection tasks. They support a comprehensive range of modes, making them suitable for diverse applications, from development to deployment.

Model Type Pre-trained Weights Task Inference Validation Training Export
YOLOv5u yolov5nu, yolov5su, yolov5mu, yolov5lu, yolov5xu, yolov5n6u, yolov5s6u, yolov5m6u, yolov5l6u, yolov5x6u Object Detection ✅ ✅ ✅ ✅

This table provides a detailed overview of the YOLOv5u model variants, highlighting their applicability in object detection tasks and support for various operational modes such as Inference, Validation, Training, and Export. This comprehensive support ensures that users can fully leverage the capabilities of YOLOv5u models in a wide range of object detection scenarios.

Performance Metrics

!!! Performance

=== "Detection"

See [Detection Docs](../tasks/detect.md) for usage examples with these models trained on [COCO](../datasets/detect/coco.md), which include 80 pre-trained classes.

| Model                                                                                       | YAML                                                                                                           | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
|---------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|-----------------------|----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
| [yolov5nu.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5nu.pt)   | [yolov5n.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml)     | 640                   | 34.3                 | 73.6                           | 1.06                                | 2.6                | 7.7               |
| [yolov5su.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5su.pt)   | [yolov5s.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml)     | 640                   | 43.0                 | 120.7                          | 1.27                                | 9.1                | 24.0              |
| [yolov5mu.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5mu.pt)   | [yolov5m.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml)     | 640                   | 49.0                 | 233.9                          | 1.86                                | 25.1               | 64.2              |
| [yolov5lu.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5lu.pt)   | [yolov5l.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml)     | 640                   | 52.2                 | 408.4                          | 2.50                                | 53.2               | 135.0             |
| [yolov5xu.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5xu.pt)   | [yolov5x.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml)     | 640                   | 53.2                 | 763.2                          | 3.81                                | 97.2               | 246.4             |
|                                                                                             |                                                                                                                |                       |                      |                                |                                     |                    |                   |
| [yolov5n6u.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5n6u.pt) | [yolov5n6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280                  | 42.1                 | 211.0                          | 1.83                                | 4.3                | 7.8               |
| [yolov5s6u.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5s6u.pt) | [yolov5s6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280                  | 48.6                 | 422.6                          | 2.34                                | 15.3               | 24.6              |
| [yolov5m6u.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5m6u.pt) | [yolov5m6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280                  | 53.6                 | 810.9                          | 4.36                                | 41.2               | 65.7              |
| [yolov5l6u.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5l6u.pt) | [yolov5l6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280                  | 55.7                 | 1470.9                         | 5.47                                | 86.1               | 137.4             |
| [yolov5x6u.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5x6u.pt) | [yolov5x6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280                  | 56.8                 | 2436.5                         | 8.98                                | 155.4              | 250.7             |

Usage Examples

This example provides simple YOLOv5 training and inference examples. For full documentation on these and other modes see the Predict, Train, Val and Export docs pages.

!!! Example

=== "Python"

    PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:

    ```python
    from ultralytics import YOLO

    # Load a COCO-pretrained YOLOv5n model
    model = YOLO("yolov5n.pt")

    # Display model information (optional)
    model.info()

    # Train the model on the COCO8 example dataset for 100 epochs
    results = model.train(data="coco8.yaml", epochs=100, imgsz=640)

    # Run inference with the YOLOv5n model on the 'bus.jpg' image
    results = model("path/to/bus.jpg")
    ```

=== "CLI"

    CLI commands are available to directly run the models:

    ```bash
    # Load a COCO-pretrained YOLOv5n model and train it on the COCO8 example dataset for 100 epochs
    yolo train model=yolov5n.pt data=coco8.yaml epochs=100 imgsz=640

    # Load a COCO-pretrained YOLOv5n model and run inference on the 'bus.jpg' image
    yolo predict model=yolov5n.pt source=path/to/bus.jpg
    ```

Citations and Acknowledgements

If you use YOLOv5 or YOLOv5u in your research, please cite the Ultralytics YOLOv5 repository as follows:

!!! Quote ""

=== "BibTeX"

    ```bibtex
    @software{yolov5,
      title = {Ultralytics YOLOv5},
      author = {Glenn Jocher},
      year = {2020},
      version = {7.0},
      license = {AGPL-3.0},
      url = {https://github.com/ultralytics/yolov5},
      doi = {10.5281/zenodo.3908559},
      orcid = {0000-0001-5950-6979}
    }
    ```

Please note that YOLOv5 models are provided under AGPL-3.0 and Enterprise licenses.

FAQ

What is YOLOv5u and how does it differ from YOLOv5?

YOLOv5u is an advanced version of the YOLOv5 object detection model developed by Ultralytics. It introduces an anchor-free, objectness-free split head, a feature adopted from the YOLOv8 models. This architectural change enhances the model's accuracy-speed tradeoff, making it more efficient and flexible for various object detection tasks. Learn more about these features in the YOLOv5 Overview.

Why should I use the anchor-free split head in YOLOv5u?

The anchor-free split head in YOLOv5u offers several advantages:

  • Flexibility: It alleviates the need for predefined anchor boxes, making the model more adaptable to diverse object scales and shapes.
  • Simplicity: Reducing dependencies on anchor boxes simplifies the model architecture, potentially decreasing the computational load.
  • Performance: Empirical results show enhanced performance in terms of accuracy and speed, making it suitable for real-time applications.

For detailed information, see the Anchor-free Split Ultralytics Head section.

How can I deploy the YOLOv5u model for real-time object detection?

Deploying YOLOv5u for real-time object detection involves several steps:

  1. Load the Model:

    from ultralytics import YOLO

model = YOLO("yolov5u.pt")

  2. Run Inference:

    results = model("path/to/image.jpg")

For a comprehensive guide, refer to the Usage Examples section.

What are the pre-trained model variants available for YOLOv5u?

YOLOv5u offers a variety of pre-trained models to cater to different needs:

  • YOLOv5nu
  • YOLOv5su
  • YOLOv5mu
  • YOLOv5lu
  • YOLOv5xu
  • YOLOv5n6u
  • YOLOv5s6u
  • YOLOv5m6u
  • YOLOv5l6u
  • YOLOv5x6u

These models support tasks like detection and offer various modes such as Inference, Validation, Training, and Export. For detailed metrics, see the Performance Metrics section.

How do YOLOv5u models perform on different hardware setups?

YOLOv5u models have been evaluated on both CPU and GPU hardware, demonstrating competitive performance metrics across various setups. For example:

  • YOLOv5nu.pt:

    • Speed (CPU ONNX): 73.6 ms
    • Speed (A100 TensorRT): 1.06 ms
    • mAP (50-95): 34.3

  • YOLOv5lu.pt:

    • Speed (CPU ONNX): 408.4 ms
    • Speed (A100 TensorRT): 2.50 ms
    • mAP (50-95): 52.2

For more detailed performance metrics, visit the Performance Metrics section.