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
Benchmark mode is used to profile the speed and accuracy of various export formats for YOLOv8. The benchmarks
provide information on the size of the exported format, its mAP50-95 metrics (for object detection, segmentation and pose)
or accuracy_top5 metrics (for classification), and the inference time in milliseconds per image across various export
formats like ONNX, OpenVINO, TensorRT and others. This information can help users choose the optimal export format for
their specific use case based on their requirements for speed and accuracy.
!!! 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 | Value | Description |
|---|---|---|
model |
None |
path to model file, i.e. yolov8n.pt, yolov8n.yaml |
data |
None |
path to YAML referencing the benchmarking dataset (under val label) |
imgsz |
640 |
image size as scalar or (h, w) list, i.e. (640, 480) |
half |
False |
FP16 quantization |
int8 |
False |
INT8 quantization |
device |
None |
device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu |
verbose |
False |
do not continue on error (bool), or val floor threshold (float) |
Export Formats
Benchmarks will attempt to run automatically on all possible export formats below.
| Format | format Argument |
Model | Metadata | Arguments |
|---|---|---|---|---|
| PyTorch | - | yolov8n.pt |
✅ | - |
| TorchScript | torchscript |
yolov8n.torchscript |
✅ | imgsz, optimize |
| ONNX | onnx |
yolov8n.onnx |
✅ | imgsz, half, dynamic, simplify, opset |
| OpenVINO | openvino |
yolov8n_openvino_model/ |
✅ | imgsz, half |
| TensorRT | engine |
yolov8n.engine |
✅ | imgsz, half, dynamic, simplify, workspace |
| CoreML | coreml |
yolov8n.mlpackage |
✅ | imgsz, half, int8, nms |
| TF SavedModel | saved_model |
yolov8n_saved_model/ |
✅ | imgsz, keras |
| TF GraphDef | pb |
yolov8n.pb |
❌ | imgsz |
| TF Lite | tflite |
yolov8n.tflite |
✅ | imgsz, half, int8 |
| TF Edge TPU | edgetpu |
yolov8n_edgetpu.tflite |
✅ | imgsz |
| TF.js | tfjs |
yolov8n_web_model/ |
✅ | imgsz |
| PaddlePaddle | paddle |
yolov8n_paddle_model/ |
✅ | imgsz |
| ncnn | ncnn |
yolov8n_ncnn_model/ |
✅ | imgsz, half |
See full export details in the Export page.