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14
README.md
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@ -29,12 +29,12 @@ Over the past years, YOLOs have emerged as the predominant paradigm in the field
COCO
| Model | Test Size | #Params | FLOPs | AP<sup>val</sup> | Latency |
|:---------------|:----:|:---:|:--:|:--:|:--:|
| [YOLOv10-N](https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10n.pt) | 640 | 2.3M | 6.7G | 38.5% | 1.84ms |
| [YOLOv10-S](https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10s.pt) | 640 | 7.2M | 21.6G | 46.3% | 2.49ms |
| [YOLOv10-M](https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10m.pt) | 640 | 15.4M | 59.1G | 51.1% | 4.74ms |
| [YOLOv10-B](https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10b.pt) | 640 | 19.1M | 92.0G | 52.5% | 5.74ms |
| [YOLOv10-L](https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10l.pt) | 640 | 24.4M | 120.3G | 53.2% | 7.28ms |
| [YOLOv10-X](https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10x.pt) | 640 | 29.5M | 160.4G | 54.4% | 10.70ms |
| [YOLOv10-N](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10n.pt) | 640 | 2.3M | 6.7G | 38.5% | 1.84ms |
| [YOLOv10-S](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10s.pt) | 640 | 7.2M | 21.6G | 46.3% | 2.49ms |
| [YOLOv10-M](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10m.pt) | 640 | 15.4M | 59.1G | 51.1% | 4.74ms |
| [YOLOv10-B](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10b.pt) | 640 | 19.1M | 92.0G | 52.5% | 5.74ms |
| [YOLOv10-L](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10l.pt) | 640 | 24.4M | 120.3G | 53.2% | 7.28ms |
| [YOLOv10-X](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10x.pt) | 640 | 29.5M | 160.4G | 54.4% | 10.70ms |
## Installation
`conda` virtual environment is recommended.
@ -46,7 +46,7 @@ pip install -e .
```
## Validation
[`yolov10n.pt`](https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10n.pt) [`yolov10s.pt`](https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10s.pt) [`yolov10m.pt`](https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10m.pt) [`yolov10b.pt`](https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10b.pt) [`yolov10l.pt`](https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10l.pt) [`yolov10x.pt`](https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10x.pt)
[`yolov10n.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10n.pt) [`yolov10s.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10s.pt) [`yolov10m.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10m.pt) [`yolov10b.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10b.pt) [`yolov10l.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10l.pt) [`yolov10x.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10x.pt)
```
yolo val model=yolov10n/s/m/b/l/x.pt data=coco.yaml batch=256
```

3
ultralytics/engine/validator.py
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@ -196,7 +196,8 @@ class BaseValidator:
self.check_stats(stats)
self.speed = dict(zip(self.speed.keys(), (x.t / len(self.dataloader.dataset) * 1e3 for x in dt)))
self.finalize_metrics()
self.print_results()
if not (self.args.save_json and self.is_coco and len(self.jdict)):
self.print_results()
self.run_callbacks("on_val_end")
if self.training:
model.float()