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Add PP-YOLOE+ Params and FLOPs (#164)
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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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@glenn-jocher @UltralyticsAssistant
glenn-jocher and UltralyticsAssistant authored Jan 28, 2025
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4 changes: 2 additions & 2 deletions docs/en/compare/damo-yolo-vs-yolov5.md
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Expand Up @@ -57,7 +57,7 @@ YOLOv5 is ideally suited for a wide range of applications due to its versatility
The table below provides a comparative overview of the performance metrics for different sizes of DAMO-YOLO and YOLOv5 models, highlighting key differences in mAP, speed, and model complexity.

| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ---------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | ----- |
| ---------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| DAMO-YOLOt | 640 | 42.0 | - | 2.32 | 8.5 | 18.1 |
| DAMO-YOLOs | 640 | 46.0 | - | 3.45 | 16.3 | 37.8 |
| DAMO-YOLOm | 640 | 49.2 | - | 5.09 | 28.2 | 61.8 |
Expand All @@ -67,7 +67,7 @@ The table below provides a comparative overview of the performance metrics for d
| YOLOv5s | 640 | 37.4 | 120.7 | 1.92 | 9.1 | 24.0 |
| YOLOv5m | 640 | 45.4 | 233.9 | 4.03 | 25.1 | 64.2 |
| YOLOv5l | 640 | 49.0 | 408.4 | 6.61 | 53.2 | 135.0 |
| YOLOv5x | 640 | 50.7 | 763.2 | 11.89 | 11.89 | 97.2 | 246.4 |
| YOLOv5x | 640 | 50.7 | 763.2 | 11.89 | 97.2 | 246.4 |

**Key Observations:**

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4 changes: 2 additions & 2 deletions docs/en/compare/pp-yoloe-vs-yolov5.md
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Expand Up @@ -93,7 +93,7 @@ YOLOv5's versatility makes it suitable for a wide array of applications, includi
## Performance Table

| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ---------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | ----- |
| ---------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| PP-YOLOE+t | 640 | 39.9 | - | 2.84 | 4.85 | 19.15 |
| PP-YOLOE+s | 640 | 43.7 | - | 2.62 | 7.93 | 17.36 |
| PP-YOLOE+m | 640 | 49.8 | - | 5.56 | 23.43 | 49.91 |
Expand All @@ -104,7 +104,7 @@ YOLOv5's versatility makes it suitable for a wide array of applications, includi
| YOLOv5s | 640 | 37.4 | 120.7 | 1.92 | 9.1 | 24.0 |
| YOLOv5m | 640 | 45.4 | 233.9 | 4.03 | 25.1 | 64.2 |
| YOLOv5l | 640 | 49.0 | 408.4 | 6.61 | 53.2 | 135.0 |
| YOLOv5x | 640 | 50.7 | 763.2 | 11.89 | 11.89 | 97.2 | 246.4 |
| YOLOv5x | 640 | 50.7 | 763.2 | 11.89 | 97.2 | 246.4 |

## Conclusion

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18 changes: 9 additions & 9 deletions docs/en/compare/yolo11-vs-yolov5.md
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Expand Up @@ -90,18 +90,18 @@ YOLOv5 is widely used in applications where speed and reliability are paramount:
## Model Comparison Table

| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | ----- |
| ------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| YOLO11n | 640 | 39.5 | 56.1 | 1.5 | 2.6 | 6.5 |
| YOLO11s | 640 | 47.0 | 90.0 | 2.5 | 9.4 | 21.5 |
| YOLO11m | 640 | 51.5 | 183.2 | 4.7 | 20.1 | 20.1 | 68.0 |
| YOLO11l | 640 | 53.4 | 238.6 | 6.2 | 25.3 | 25.3 | 86.9 |
| YOLO11x | 640 | 54.7 | 462.8 | 11.3 | 56.9 | 56.9 | 194.9 |
| YOLO11m | 640 | 51.5 | 183.2 | 4.7 | 20.1 | 68.0 |
| YOLO11l | 640 | 53.4 | 238.6 | 6.2 | 25.3 | 86.9 |
| YOLO11x | 640 | 54.7 | 462.8 | 11.3 | 56.9 | 194.9 |
| | | | | | | |
| YOLOv5n | 640 | 28.0 | 73.6 | 1.12 | 2.6 | 2.6 | 7.7 |
| YOLOv5s | 640 | 37.4 | 120.7 | 1.92 | 9.1 | 9.1 | 24.0 |
| YOLOv5m | 640 | 45.4 | 233.9 | 4.03 | 25.1 | 25.1 | 64.2 |
| YOLOv5l | 640 | 49.0 | 408.4 | 6.61 | 53.2 | 53.2 | 135.0 |
| YOLOv5x | 640 | 50.7 | 763.2 | 11.89 | 97.2 | 97.2 | 246.4 |
| YOLOv5n | 640 | 28.0 | 73.6 | 1.12 | 2.6 | 7.7 |
| YOLOv5s | 640 | 37.4 | 120.7 | 1.92 | 9.1 | 24.0 |
| YOLOv5m | 640 | 45.4 | 233.9 | 4.03 | 25.1 | 64.2 |
| YOLOv5l | 640 | 49.0 | 408.4 | 6.61 | 53.2 | 135.0 |
| YOLOv5x | 640 | 50.7 | 763.2 | 11.89 | 97.2 | 246.4 |

## Key Differences Summarized

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4 changes: 2 additions & 2 deletions docs/en/compare/yolov5-vs-yolov6.md
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Expand Up @@ -80,12 +80,12 @@ Both YOLOv5 and YOLOv6-3.0 are trained using similar methodologies common in obj
## Model Comparison Table

| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ----------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | ----- |
| ----------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| YOLOv5n | 640 | 28.0 | 73.6 | 1.12 | 2.6 | 7.7 |
| YOLOv5s | 640 | 37.4 | 120.7 | 1.92 | 9.1 | 24.0 |
| YOLOv5m | 640 | 45.4 | 233.9 | 4.03 | 25.1 | 64.2 |
| YOLOv5l | 640 | 49.0 | 408.4 | 6.61 | 53.2 | 135.0 |
| YOLOv5x | 640 | 50.7 | 763.2 | 11.89 | 11.89 | 97.2 | 246.4 |
| YOLOv5x | 640 | 50.7 | 763.2 | 11.89 | 97.2 | 246.4 |
| | | | | | | |
| YOLOv6-3.0n | 640 | 37.5 | - | 1.17 | 4.7 | 11.4 |
| YOLOv6-3.0s | 640 | 45.0 | - | 2.66 | 18.5 | 45.3 |
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4 changes: 2 additions & 2 deletions docs/en/compare/yolov6-vs-yolov5.md
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Expand Up @@ -87,7 +87,7 @@ YOLOv6-3.0 is designed for scenarios where high accuracy and fast inference are
## Performance Comparison Table

| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ----------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | ----- |
| ----------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| YOLOv6-3.0n | 640 | 37.5 | - | 1.17 | 4.7 | 11.4 |
| YOLOv6-3.0s | 640 | 45.0 | - | 2.66 | 18.5 | 45.3 |
| YOLOv6-3.0m | 640 | 50.0 | - | 5.28 | 34.9 | 85.8 |
Expand All @@ -97,7 +97,7 @@ YOLOv6-3.0 is designed for scenarios where high accuracy and fast inference are
| YOLOv5s | 640 | 37.4 | 120.7 | 1.92 | 9.1 | 24.0 |
| YOLOv5m | 640 | 45.4 | 233.9 | 4.03 | 25.1 | 64.2 |
| YOLOv5l | 640 | 49.0 | 408.4 | 6.61 | 53.2 | 135.0 |
| YOLOv5x | 640 | 50.7 | 763.2 | 11.89 | 11.89 | 97.2 | 246.4 |
| YOLOv5x | 640 | 50.7 | 763.2 | 11.89 | 97.2 | 246.4 |

## Conclusion

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12 changes: 6 additions & 6 deletions docs/en/compare/yolov7-vs-efficientdet.md
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Expand Up @@ -74,18 +74,18 @@ Performance metrics are crucial for evaluating object detection models. Key metr
## Model Comparison Table

| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| --------------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | ----- |
| --------------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| YOLOv7l | 640 | 51.4 | - | 6.84 | 36.9 | 104.7 |
| YOLOv7x | 640 | 53.1 | - | 11.57 | 71.3 | 189.9 |
| | | | | | | |
| EfficientDet-d0 | 640 | 34.6 | 10.2 | 3.92 | 3.9 | 2.54 |
| EfficientDet-d1 | 640 | 40.5 | 13.5 | 7.31 | 6.6 | 6.1 |
| EfficientDet-d2 | 640 | 43.0 | 17.7 | 10.92 | 8.1 | 11.0 |
| EfficientDet-d3 | 640 | 47.5 | 28.0 | 28.0 | 19.59 | 12.0 | 24.9 |
| EfficientDet-d4 | 640 | 49.7 | 42.8 | 42.8 | 33.55 | 20.7 | 55.2 |
| EfficientDet-d5 | 640 | 51.5 | 72.5 | 72.5 | 67.86 | 33.7 | 130.0 |
| EfficientDet-d6 | 640 | 52.6 | 92.8 | 92.8 | 89.29 | 51.9 | 226.0 |
| EfficientDet-d7 | 640 | 53.7 | 122.0 | 122.0 | 128.07 | 51.9 | 325.0 |
| EfficientDet-d3 | 640 | 47.5 | 28.0 | 19.59 | 12.0 | 24.9 |
| EfficientDet-d4 | 640 | 49.7 | 42.8 | 33.55 | 20.7 | 55.2 |
| EfficientDet-d5 | 640 | 51.5 | 72.5 | 67.86 | 33.7 | 130.0 |
| EfficientDet-d6 | 640 | 52.6 | 92.8 | 89.29 | 51.9 | 226.0 |
| EfficientDet-d7 | 640 | 53.7 | 122.0 | 128.07 | 51.9 | 325.0 |

## Conclusion

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4 changes: 2 additions & 2 deletions docs/en/compare/yolov8-vs-rtdetr.md
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Expand Up @@ -79,10 +79,10 @@ RTDETRv2 is well-suited for applications where understanding the broader context
## Model Comparison Table

| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ---------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | ---- |
| ---------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| YOLOv8n | 640 | 37.3 | 80.4 | 1.47 | 3.2 | 8.7 |
| YOLOv8s | 640 | 44.9 | 128.4 | 2.66 | 11.2 | 28.6 |
| YOLOv8m | 640 | 50.2 | 234.7 | 5.86 | 5.86 | 25.9 | 78.9 |
| YOLOv8m | 640 | 50.2 | 234.7 | 5.86 | 25.9 | 78.9 |
| YOLOv8l | 640 | 52.9 | 375.2 | 9.06 | 43.7 | 165.2 |
| YOLOv8x | 640 | 53.9 | 479.1 | 14.37 | 68.2 | 257.8 |
| | | | | | | |
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