YOLOv10 outperforms previous YOLO versions and other state-of-the-art models in terms of accuracy and efficiency. For example, YOLOv10-S is 1.8x faster than RT-DETR-R18 with similar AP on the COCO dataset, and YOLOv10-B has 46% less latency and 25% fewer parameters than YOLOv9-C with the same performance.
YOLOv10 outperforms previous YOLO versions and other state-of-the-art models in terms of accuracy and efficiency. For example, YOLOv10s is 1.8x faster than RT-DETR-R18 with similar AP on the COCO dataset, and YOLOv10b has 46% less latency and 25% fewer parameters than YOLOv9-C with the same performance.
@ -264,7 +264,7 @@ For easy inference, you can use the Ultralytics YOLO Python library or the comma
```python
from ultralytics import YOLO
# Load the pre-trained YOLOv10-N model
# Load the pre-trained YOLOv10n model
model = YOLO("yolov10n.pt")
results = model("image.jpg")
results[0].show()
@ -282,12 +282,12 @@ For more usage examples, visit our [Usage Examples](#usage-examples) section.
YOLOv10 offers several model variants to cater to different use cases:
- **YOLOv10-N**: Suitable for extremely resource-constrained environments
- **YOLOv10-S**: Balances speed and accuracy
- **YOLOv10-M**: General-purpose use
- **YOLOv10-B**: Higher accuracy with increased width
- **YOLOv10-L**: High accuracy at the cost of computational resources
- **YOLOv10-X**: Maximum accuracy and performance
- **YOLOv10n**: Suitable for extremely resource-constrained environments
- **YOLOv10s**: Balances speed and accuracy
- **YOLOv10m**: General-purpose use
- **YOLOv10b**: Higher accuracy with increased width
- **YOLOv10l**: High accuracy at the cost of computational resources
- **YOLOv10x**: Maximum accuracy and performance
Each variant is designed for different computational needs and accuracy requirements, making them versatile for a variety of applications. Explore the [Model Variants](#model-variants) section for more information.
@ -301,4 +301,4 @@ YOLOv10 supports several export formats, including TorchScript, ONNX, OpenVINO,
### What are the performance benchmarks for YOLOv10 models?
YOLOv10 outperforms previous YOLO versions and other state-of-the-art models in both accuracy and efficiency. For example, YOLOv10-S is 1.8x faster than RT-DETR-R18 with a similar AP on the COCO dataset. YOLOv10-B shows 46% less latency and 25% fewer parameters than YOLOv9-C with the same performance. Detailed benchmarks can be found in the [Comparisons](#comparisons) section.
YOLOv10 outperforms previous YOLO versions and other state-of-the-art models in both accuracy and efficiency. For example, YOLOv10s is 1.8x faster than RT-DETR-R18 with a similar AP on the COCO dataset. YOLOv10b shows 46% less latency and 25% fewer parameters than YOLOv9-C with the same performance. Detailed benchmarks can be found in the [Comparisons](#comparisons) section.
@ -14,7 +14,7 @@ YOLOv7 is a state-of-the-art real-time object detector that surpasses all known
From the results in the YOLO comparison table we know that the proposed method has the best speed-accuracy trade-off comprehensively. If we compare YOLOv7-tiny-SiLU with YOLOv5-N (r6.1), our method is 127 fps faster and 10.7% more accurate on AP. In addition, YOLOv7 has 51.4% AP at frame rate of 161 fps, while PPYOLOE-L with the same AP has only 78 fps frame rate. In terms of parameter usage, YOLOv7 is 41% less than PPYOLOE-L.
Glenn Jocher
3 weeks ago
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GitHub