---
comments: true
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.
keywords: model benchmarking, YOLO11, Ultralytics, performance evaluation, export formats, ONNX, TensorRT, OpenVINO, CoreML, TensorFlow, optimization, mAP50-95, inference time
---
# Model Benchmarking with Ultralytics YOLO
## Benchmark Visualization
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## Introduction
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.
Watch: Benchmark Ultralytics YOLO11 Models | How to Compare Model Performance on Different Hardware?
## Why Is Benchmarking Crucial?
- **Informed Decisions:** Gain insights into the trade-offs between speed and accuracy.
- **Resource Allocation:** Understand how different export formats perform on different hardware.
- **Optimization:** Learn which export format offers the best performance for your specific use case.
- **Cost Efficiency:** Make more efficient use of hardware resources based on benchmark results.
### Key Metrics in Benchmark Mode
- **mAP50-95:** For [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and pose estimation.
- **accuracy_top5:** For [image classification](https://www.ultralytics.com/glossary/image-classification).
- **Inference Time:** Time taken for each image in milliseconds.
### Supported Export Formats
- **ONNX:** For optimal CPU performance
- **TensorRT:** For maximal GPU efficiency
- **OpenVINO:** For Intel hardware optimization
- **CoreML, TensorFlow SavedModel, and More:** For diverse deployment needs.
!!! tip
* Export to ONNX or OpenVINO for up to 3x CPU speedup.
* Export to TensorRT for up to 5x GPU speedup.
## Usage Examples
Run YOLO11n benchmarks on all supported export formats including ONNX, TensorRT etc. See Arguments section below for a full list of export arguments.
!!! example
=== "Python"
```python
from ultralytics.utils.benchmarks import benchmark
# Benchmark on GPU
benchmark(model="yolo11n.pt", data="coco8.yaml", imgsz=640, half=False, device=0)
# Benchmark specific export format
benchmark(model="yolo11n.pt", data="coco8.yaml", imgsz=640, format="onnx")
```
=== "CLI"
```bash
yolo benchmark model=yolo11n.pt data='coco8.yaml' imgsz=640 half=False device=0
# Benchmark specific export format
yolo benchmark model=yolo11n.pt data='coco8.yaml' imgsz=640 format=onnx
```
## Arguments
Arguments such as `model`, `data`, `imgsz`, `half`, `device`, `verbose` and `format` provide users with the flexibility to fine-tune the benchmarks to their specific needs and compare the performance of different export formats with ease.
| Key | Default Value | Description |
| --------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `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. |
| `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"`. |
| `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)`. |
| `half` | `False` | Enables FP16 (half-precision) inference, reducing memory usage and possibly increasing speed on compatible hardware. Use `half=True` to enable. |
| `int8` | `False` | Activates INT8 quantization for further optimized performance on supported devices, especially useful for edge devices. Set `int8=True` to use. |
| `device` | `None` | Defines the computation device(s) for benchmarking, such as `"cpu"` or `"cuda:0"`. |
| `verbose` | `False` | Controls the level of detail in logging output. Set `verbose=True` for detailed logs. |
| `format` | `''` | Benchmark the model on a single export format. i.e `format=onnx` |
## Export Formats
Benchmarks will attempt to run automatically on all possible export formats listed below. Alternatively, you can run benchmarks for a specific format by using the `format` argument, which accepts any of the formats mentioned below.
{% include "macros/export-table.md" %}
See full `export` details in the [Export](../modes/export.md) page.
## FAQ
### How do I benchmark my YOLO11 model's performance using Ultralytics?
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:
!!! example
=== "Python"
```python
from ultralytics.utils.benchmarks import benchmark
# Benchmark on GPU
benchmark(model="yolo11n.pt", data="coco8.yaml", imgsz=640, half=False, device=0)
```
=== "CLI"
```bash
yolo benchmark model=yolo11n.pt data='coco8.yaml' imgsz=640 half=False device=0
```
For more details on benchmark arguments, visit the [Arguments](#arguments) section.
### What are the benefits of exporting YOLO11 models to different formats?
Exporting YOLO11 models to different formats such as [ONNX](https://docs.ultralytics.com/integrations/onnx/), [TensorRT](https://docs.ultralytics.com/integrations/tensorrt/), and [OpenVINO](https://docs.ultralytics.com/integrations/openvino/) allows you to optimize performance based on your deployment environment. For instance:
- **ONNX:** Provides up to 3x CPU speedup.
- **TensorRT:** Offers up to 5x GPU speedup.
- **OpenVINO:** Specifically optimized for Intel hardware.
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.
### Why is benchmarking crucial in evaluating YOLO11 models?
Benchmarking your YOLO11 models is essential for several reasons:
- **Informed Decisions:** Understand the trade-offs between speed and accuracy.
- **Resource Allocation:** Gauge the performance across different hardware options.
- **Optimization:** Determine which export format offers the best performance for specific use cases.
- **Cost Efficiency:** Optimize hardware usage based on benchmark results.
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.
### Which export formats are supported by YOLO11, and what are their advantages?
YOLO11 supports a variety of export formats, each tailored for specific hardware and use cases:
- **ONNX:** Best for CPU performance.
- **TensorRT:** Ideal for GPU efficiency.
- **OpenVINO:** Optimized for Intel hardware.
- **CoreML & [TensorFlow](https://www.ultralytics.com/glossary/tensorflow):** Useful for iOS and general ML applications.
For a complete list of supported formats and their respective advantages, check out the [Supported Export Formats](#supported-export-formats) section.
### What arguments can I use to fine-tune my YOLO11 benchmarks?
When running benchmarks, several arguments can be customized to suit specific needs:
- **model:** Path to the model file (e.g., "yolo11n.pt").
- **data:** Path to a YAML file defining the dataset (e.g., "coco8.yaml").
- **imgsz:** The input image size, either as a single integer or a tuple.
- **half:** Enable FP16 inference for better performance.
- **int8:** Activate INT8 quantization for edge devices.
- **device:** Specify the computation device (e.g., "cpu", "cuda:0").
- **verbose:** Control the level of logging detail.
For a full list of arguments, refer to the [Arguments](#arguments) section.
## Benchmark Visualization
!!! tip "Refresh Browser"
You may need to refresh the page to view the graphs correctly due to potential cookie issues.
## Introduction
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.