---
comments: true
description: Learn how to profile speed and accuracy of YOLOv8 across various export formats; get insights on mAP50-95, accuracy_top5 metrics, and more.
keywords: Ultralytics, YOLOv8, benchmarking, speed profiling, accuracy profiling, mAP50-95, accuracy_top5, ONNX, OpenVINO, TensorRT, YOLO export formats
---

# Model Benchmarking with Ultralytics YOLO

<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics YOLO ecosystem and integrations">

## 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 YOLOv8 serves this purpose by providing a robust framework for assessing the speed and accuracy of your model across a range of export formats.

<p align="center">
  <br>
  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/j8uQc0qB91s?start=105"
    title="YouTube video player" frameborder="0"
    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
    allowfullscreen>
  </iframe>
  <br>
  <strong>Watch:</strong> Ultralytics Modes Tutorial: Benchmark
</p>

## 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, segmentation, and pose estimation.
- **accuracy_top5:** For 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 "Tip"

    * Export to ONNX or OpenVINO for up to 3x CPU speedup.
    * Export to TensorRT for up to 5x GPU speedup.

## Usage Examples

Run YOLOv8n 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="yolov8n.pt", data="coco8.yaml", imgsz=640, half=False, device=0)
        ```

    === "CLI"

        ```bash
        yolo benchmark model=yolov8n.pt data='coco8.yaml' imgsz=640 half=False device=0
        ```

## Arguments

Arguments such as `model`, `data`, `imgsz`, `half`, `device`, and `verbose` 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., `"yolov8n.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. 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"`, `"cuda:0"`, or a list of devices like `"cuda:0,1"` for multi-GPU setups.     |
| `verbose` | `False`       | Controls the level of detail in logging output. A boolean value; set `verbose=True` for detailed logs or a float for thresholding errors.         |

## Export Formats

Benchmarks will attempt to run automatically on all possible export formats below.

| Format                                            | `format` Argument | Model                     | Metadata | Arguments                                                            |
|---------------------------------------------------|-------------------|---------------------------|----------|----------------------------------------------------------------------|
| [PyTorch](https://pytorch.org/)                   | -                 | `yolov8n.pt`              | ✅        | -                                                                    |
| [TorchScript](../integrations/torchscript.md)     | `torchscript`     | `yolov8n.torchscript`     | ✅        | `imgsz`, `optimize`, `batch`                                         |
| [ONNX](../integrations/onnx.md)                   | `onnx`            | `yolov8n.onnx`            | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch`             |
| [OpenVINO](../integrations/openvino.md)           | `openvino`        | `yolov8n_openvino_model/` | ✅        | `imgsz`, `half`, `int8`, `batch`                                     |
| [TensorRT](../integrations/tensorrt.md)           | `engine`          | `yolov8n.engine`          | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` |
| [CoreML](../integrations/coreml.md)               | `coreml`          | `yolov8n.mlpackage`       | ✅        | `imgsz`, `half`, `int8`, `nms`, `batch`                              |
| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model`     | `yolov8n_saved_model/`    | ✅        | `imgsz`, `keras`, `int8`, `batch`                                    |
| [TF GraphDef](../integrations/tf-graphdef.md)     | `pb`              | `yolov8n.pb`              | ❌        | `imgsz`, `batch`                                                     |
| [TF Lite](../integrations/tflite.md)              | `tflite`          | `yolov8n.tflite`          | ✅        | `imgsz`, `half`, `int8`, `batch`                                     |
| [TF Edge TPU](../integrations/edge-tpu.md)        | `edgetpu`         | `yolov8n_edgetpu.tflite`  | ✅        | `imgsz`, `batch`                                                     |
| [TF.js](../integrations/tfjs.md)                  | `tfjs`            | `yolov8n_web_model/`      | ✅        | `imgsz`, `half`, `int8`, `batch`                                     |
| [PaddlePaddle](../integrations/paddlepaddle.md)   | `paddle`          | `yolov8n_paddle_model/`   | ✅        | `imgsz`, `batch`                                                     |
| [NCNN](../integrations/ncnn.md)                   | `ncnn`            | `yolov8n_ncnn_model/`     | ✅        | `imgsz`, `half`, `batch`                                             |

See full `export` details in the [Export](../modes/export.md) page.
