YOLO11 Tasks, Modes, Usage, Macros and Solutions Updates (#16593)
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---
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comments: true
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description: Learn how to evaluate your YOLOv8 model's performance in real-world scenarios using benchmark mode. Optimize speed, accuracy, and resource allocation across export formats.
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keywords: model benchmarking, YOLOv8, Ultralytics, performance evaluation, export formats, ONNX, TensorRT, OpenVINO, CoreML, TensorFlow, optimization, mAP50-95, inference time
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description: Learn how to evaluate your YOLO11 model's performance in real-world scenarios using benchmark mode. Optimize speed, accuracy, and resource allocation across export formats.
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keywords: model benchmarking, YOLO11, Ultralytics, performance evaluation, export formats, ONNX, TensorRT, OpenVINO, CoreML, TensorFlow, optimization, mAP50-95, inference time
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---
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# Model Benchmarking with Ultralytics YOLO
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@ -10,7 +10,7 @@ keywords: model benchmarking, YOLOv8, Ultralytics, performance evaluation, expor
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## Introduction
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Once your model is trained and validated, the next logical step is to evaluate its performance in various real-world scenarios. Benchmark mode in Ultralytics YOLOv8 serves this purpose by providing a robust framework for assessing the speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) of your model across a range of export formats.
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Once your model is trained and validated, the next logical step is to evaluate its performance in various real-world scenarios. Benchmark mode in Ultralytics YOLO11 serves this purpose by providing a robust framework for assessing the speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) of your model across a range of export formats.
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<p align="center">
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<br>
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@ -50,7 +50,7 @@ Once your model is trained and validated, the next logical step is to evaluate i
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## Usage Examples
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Run YOLOv8n benchmarks on all supported export formats including ONNX, TensorRT etc. See Arguments section below for a full list of export arguments.
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Run YOLO11n benchmarks on all supported export formats including ONNX, TensorRT etc. See Arguments section below for a full list of export arguments.
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!!! example
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@ -60,13 +60,13 @@ Run YOLOv8n benchmarks on all supported export formats including ONNX, TensorRT
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from ultralytics.utils.benchmarks import benchmark
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# Benchmark on GPU
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benchmark(model="yolov8n.pt", data="coco8.yaml", imgsz=640, half=False, device=0)
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benchmark(model="yolo11n.pt", data="coco8.yaml", imgsz=640, half=False, device=0)
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```
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=== "CLI"
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```bash
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yolo benchmark model=yolov8n.pt data='coco8.yaml' imgsz=640 half=False device=0
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yolo benchmark model=yolo11n.pt data='coco8.yaml' imgsz=640 half=False device=0
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```
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## Arguments
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@ -75,7 +75,7 @@ Arguments such as `model`, `data`, `imgsz`, `half`, `device`, and `verbose` prov
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| Key | Default Value | Description |
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| --------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `model` | `None` | Specifies the path to the model file. Accepts both `.pt` and `.yaml` formats, e.g., `"yolov8n.pt"` for pre-trained models or configuration files. |
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| `model` | `None` | Specifies the path to the model file. Accepts both `.pt` and `.yaml` formats, e.g., `"yolo11n.pt"` for pre-trained models or configuration files. |
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| `data` | `None` | Path to a YAML file defining the dataset for benchmarking, typically including paths and settings for [validation data](https://www.ultralytics.com/glossary/validation-data). Example: `"coco8.yaml"`. |
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| `imgsz` | `640` | The input image size for the model. Can be a single integer for square images or a tuple `(width, height)` for non-square, e.g., `(640, 480)`. |
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| `half` | `False` | Enables FP16 (half-precision) inference, reducing memory usage and possibly increasing speed on compatible hardware. Use `half=True` to enable. |
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@ -93,9 +93,9 @@ See full `export` details in the [Export](../modes/export.md) page.
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## FAQ
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### How do I benchmark my YOLOv8 model's performance using Ultralytics?
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### How do I benchmark my YOLO11 model's performance using Ultralytics?
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Ultralytics YOLOv8 offers a Benchmark mode to assess your model's performance across different export formats. This mode provides insights into key metrics such as [mean Average Precision](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP50-95), accuracy, and inference time in milliseconds. To run benchmarks, you can use either Python or CLI commands. For example, to benchmark on a GPU:
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Ultralytics YOLO11 offers a Benchmark mode to assess your model's performance across different export formats. This mode provides insights into key metrics such as [mean Average Precision](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP50-95), accuracy, and inference time in milliseconds. To run benchmarks, you can use either Python or CLI commands. For example, to benchmark on a GPU:
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!!! example
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from ultralytics.utils.benchmarks import benchmark
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# Benchmark on GPU
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benchmark(model="yolov8n.pt", data="coco8.yaml", imgsz=640, half=False, device=0)
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benchmark(model="yolo11n.pt", data="coco8.yaml", imgsz=640, half=False, device=0)
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```
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=== "CLI"
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```bash
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yolo benchmark model=yolov8n.pt data='coco8.yaml' imgsz=640 half=False device=0
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yolo benchmark model=yolo11n.pt data='coco8.yaml' imgsz=640 half=False device=0
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```
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For more details on benchmark arguments, visit the [Arguments](#arguments) section.
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### What are the benefits of exporting YOLOv8 models to different formats?
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### What are the benefits of exporting YOLO11 models to different formats?
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Exporting YOLOv8 models to different formats such as ONNX, TensorRT, and OpenVINO allows you to optimize performance based on your deployment environment. For instance:
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Exporting YOLO11 models to different formats such as ONNX, TensorRT, and OpenVINO allows you to optimize performance based on your deployment environment. For instance:
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- **ONNX:** Provides up to 3x CPU speedup.
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- **TensorRT:** Offers up to 5x GPU speedup.
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- **OpenVINO:** Specifically optimized for Intel hardware.
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These formats enhance both the speed and accuracy of your models, making them more efficient for various real-world applications. Visit the [Export](../modes/export.md) page for complete details.
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### Why is benchmarking crucial in evaluating YOLOv8 models?
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### Why is benchmarking crucial in evaluating YOLO11 models?
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Benchmarking your YOLOv8 models is essential for several reasons:
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Benchmarking your YOLO11 models is essential for several reasons:
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- **Informed Decisions:** Understand the trade-offs between speed and accuracy.
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- **Resource Allocation:** Gauge the performance across different hardware options.
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@ -135,9 +135,9 @@ Benchmarking your YOLOv8 models is essential for several reasons:
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- **Cost Efficiency:** Optimize hardware usage based on benchmark results.
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Key metrics such as mAP50-95, Top-5 accuracy, and inference time help in making these evaluations. Refer to the [Key Metrics](#key-metrics-in-benchmark-mode) section for more information.
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### Which export formats are supported by YOLOv8, and what are their advantages?
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### Which export formats are supported by YOLO11, and what are their advantages?
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YOLOv8 supports a variety of export formats, each tailored for specific hardware and use cases:
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YOLO11 supports a variety of export formats, each tailored for specific hardware and use cases:
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- **ONNX:** Best for CPU performance.
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- **TensorRT:** Ideal for GPU efficiency.
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@ -145,11 +145,11 @@ YOLOv8 supports a variety of export formats, each tailored for specific hardware
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- **CoreML & [TensorFlow](https://www.ultralytics.com/glossary/tensorflow):** Useful for iOS and general ML applications.
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For a complete list of supported formats and their respective advantages, check out the [Supported Export Formats](#supported-export-formats) section.
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### What arguments can I use to fine-tune my YOLOv8 benchmarks?
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### What arguments can I use to fine-tune my YOLO11 benchmarks?
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When running benchmarks, several arguments can be customized to suit specific needs:
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- **model:** Path to the model file (e.g., "yolov8n.pt").
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- **model:** Path to the model file (e.g., "yolo11n.pt").
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- **data:** Path to a YAML file defining the dataset (e.g., "coco8.yaml").
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- **imgsz:** The input image size, either as a single integer or a tuple.
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- **half:** Enable FP16 inference for better performance.
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