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ultralytics 8.2.2 replace COCO128 with COCO8 (#10167)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Glenn Jocher 2024-04-18 20:47:21 -07:00 • committed by GitHub
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docs/en/integrations/openvino.md
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@ -261,14 +261,14 @@ To reproduce the Ultralytics benchmarks above on all export [formats](../modes/e
# Load a YOLOv8n PyTorch model
model = YOLO('yolov8n.pt')
# Benchmark YOLOv8n speed and accuracy on the COCO128 dataset for all all export formats
results= model.benchmarks(data='coco128.yaml')
# Benchmark YOLOv8n speed and accuracy on the COCO8 dataset for all all export formats
results= model.benchmarks(data='coco8.yaml')
```
=== "CLI"
```bash
# Benchmark YOLOv8n speed and accuracy on the COCO128 dataset for all all export formats
yolo benchmark model=yolov8n.pt data=coco128.yaml
# Benchmark YOLOv8n speed and accuracy on the COCO8 dataset for all all export formats
yolo benchmark model=yolov8n.pt data=coco8.yaml
```
Note that benchmarking results might vary based on the exact hardware and software configuration of a system, as well as the current workload of the system at the time the benchmarks are run. For the most reliable results use a dataset with a large number of images, i.e. `data='coco128.yaml' (128 val images), or `data='coco.yaml'` (5000 val images).