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162 lines
14 KiB
162 lines
14 KiB
--- |
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comments: true |
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description: Explore YOLOv5u, an advanced object detection model with optimized accuracy-speed tradeoff, featuring anchor-free Ultralytics head and various pre-trained models. |
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keywords: YOLOv5, YOLOv5u, object detection, Ultralytics, anchor-free, pre-trained models, accuracy, speed, real-time detection |
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--- |
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# YOLOv5 |
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## Overview |
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YOLOv5u represents an advancement in object detection methodologies. Originating from the foundational architecture of the [YOLOv5](https://github.com/ultralytics/yolov5) model developed by Ultralytics, YOLOv5u integrates the anchor-free, objectness-free split head, a feature previously introduced in the [YOLOv8](yolov8.md) 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. |
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## Key Features |
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- **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. |
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- **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. |
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- **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. |
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## Supported Tasks and Modes |
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The YOLOv5u models, with various pre-trained weights, excel in [Object Detection](../tasks/detect.md) tasks. They support a comprehensive range of modes, making them suitable for diverse applications, from development to deployment. |
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| Model Type | Pre-trained Weights | Task | Inference | Validation | Training | Export | |
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| ---------- | --------------------------------------------------------------------------------------------------------------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ | |
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| YOLOv5u | `yolov5nu`, `yolov5su`, `yolov5mu`, `yolov5lu`, `yolov5xu`, `yolov5n6u`, `yolov5s6u`, `yolov5m6u`, `yolov5l6u`, `yolov5x6u` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | |
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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](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md). This comprehensive support ensures that users can fully leverage the capabilities of YOLOv5u models in a wide range of object detection scenarios. |
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## Performance Metrics |
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!!! Performance |
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=== "Detection" |
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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. |
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| 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) | |
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|---------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|-----------------------|----------------------|--------------------------------|-------------------------------------|--------------------|-------------------| |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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## Usage Examples |
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This example provides simple YOLOv5 training and inference examples. For full documentation on these and other [modes](../modes/index.md) see the [Predict](../modes/predict.md), [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md) docs pages. |
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!!! Example |
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=== "Python" |
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PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python: |
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```python |
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from ultralytics import YOLO |
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# Load a COCO-pretrained YOLOv5n model |
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model = YOLO("yolov5n.pt") |
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# Display model information (optional) |
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model.info() |
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# Train the model on the COCO8 example dataset for 100 epochs |
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results = model.train(data="coco8.yaml", epochs=100, imgsz=640) |
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# Run inference with the YOLOv5n model on the 'bus.jpg' image |
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results = model("path/to/bus.jpg") |
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``` |
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=== "CLI" |
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CLI commands are available to directly run the models: |
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```bash |
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# Load a COCO-pretrained YOLOv5n model and train it on the COCO8 example dataset for 100 epochs |
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yolo train model=yolov5n.pt data=coco8.yaml epochs=100 imgsz=640 |
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# Load a COCO-pretrained YOLOv5n model and run inference on the 'bus.jpg' image |
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yolo predict model=yolov5n.pt source=path/to/bus.jpg |
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``` |
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## Citations and Acknowledgements |
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If you use YOLOv5 or YOLOv5u in your research, please cite the Ultralytics YOLOv5 repository as follows: |
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!!! Quote "" |
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=== "BibTeX" |
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```bibtex |
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@software{yolov5, |
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title = {Ultralytics YOLOv5}, |
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author = {Glenn Jocher}, |
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year = {2020}, |
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version = {7.0}, |
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license = {AGPL-3.0}, |
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url = {https://github.com/ultralytics/yolov5}, |
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doi = {10.5281/zenodo.3908559}, |
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orcid = {0000-0001-5950-6979} |
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} |
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``` |
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Please note that YOLOv5 models are provided under [AGPL-3.0](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) and [Enterprise](https://ultralytics.com/license) licenses. |
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## FAQ |
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### What is Ultralytics YOLOv5u and how does it differ from YOLOv5? |
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Ultralytics YOLOv5u is an advanced version of YOLOv5, integrating the anchor-free, objectness-free split head that enhances the accuracy-speed tradeoff for real-time object detection tasks. Unlike the traditional YOLOv5, YOLOv5u adopts an anchor-free detection mechanism, making it more flexible and adaptive in diverse scenarios. For more detailed information on its features, you can refer to the [YOLOv5 Overview](#overview). |
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### How does the anchor-free Ultralytics head improve object detection performance in YOLOv5u? |
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The anchor-free Ultralytics head in YOLOv5u improves object detection performance by eliminating the dependency on predefined anchor boxes. This results in a more flexible and adaptive detection mechanism that can handle various object sizes and shapes with greater efficiency. This enhancement directly contributes to a balanced tradeoff between accuracy and speed, making YOLOv5u suitable for real-time applications. Learn more about its architecture in the [Key Features](#key-features) section. |
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### Can I use pre-trained YOLOv5u models for different tasks and modes? |
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Yes, you can use pre-trained YOLOv5u models for various tasks such as [Object Detection](../tasks/detect.md). These models support multiple modes, including [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md). This flexibility allows users to leverage the capabilities of YOLOv5u models across different operational requirements. For a detailed overview, check the [Supported Tasks and Modes](#supported-tasks-and-modes) section. |
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### How do the performance metrics of YOLOv5u models compare on different platforms? |
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The performance metrics of YOLOv5u models vary depending on the platform and hardware used. For example, the YOLOv5nu model achieves a 34.3 mAP on COCO dataset with a speed of 73.6 ms on CPU (ONNX) and 1.06 ms on A100 TensorRT. Detailed performance metrics for different YOLOv5u models can be found in the [Performance Metrics](#performance-metrics) section, which provides a comprehensive comparison across various devices. |
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### How can I train a YOLOv5u model using the Ultralytics Python API? |
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You can train a YOLOv5u model by loading a pre-trained model and running the training command with your dataset. Here's a quick example: |
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!!! Example |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a COCO-pretrained YOLOv5n model |
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model = YOLO("yolov5n.pt") |
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# Display model information (optional) |
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model.info() |
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# Train the model on the COCO8 example dataset for 100 epochs |
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results = model.train(data="coco8.yaml", epochs=100, imgsz=640) |
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``` |
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=== "CLI" |
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```bash |
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# Load a COCO-pretrained YOLOv5n model and train it on the COCO8 example dataset for 100 epochs |
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yolo train model=yolov5n.pt data=coco8.yaml epochs=100 imgsz=640 |
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``` |
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For more detailed instructions, visit the [Usage Examples](#usage-examples) section.
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