From a43cf5cd53aca25ad4874d11037484d9c598f4fd Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Tue, 28 Jan 2025 09:37:59 +0100 Subject: [PATCH] Add PP-YOLOE+ Params and FLOPs (#164) Co-authored-by: UltralyticsAssistant --- docs/en/compare/damo-yolo-vs-yolov5.md | 4 ++-- docs/en/compare/pp-yoloe-vs-yolov5.md | 4 ++-- docs/en/compare/yolo11-vs-yolov5.md | 18 +++++++++--------- docs/en/compare/yolov5-vs-yolov6.md | 4 ++-- docs/en/compare/yolov6-vs-yolov5.md | 4 ++-- docs/en/compare/yolov7-vs-efficientdet.md | 12 ++++++------ docs/en/compare/yolov8-vs-rtdetr.md | 4 ++-- docs/en/compare/yolox-vs-efficientdet.md | 4 ++-- 8 files changed, 27 insertions(+), 27 deletions(-) diff --git a/docs/en/compare/damo-yolo-vs-yolov5.md b/docs/en/compare/damo-yolo-vs-yolov5.md index 0e699fa5f8..4d8803185a 100644 --- a/docs/en/compare/damo-yolo-vs-yolov5.md +++ b/docs/en/compare/damo-yolo-vs-yolov5.md @@ -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
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
T4 TensorRT10
(ms) | params
(M) | FLOPs
(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 | @@ -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:** diff --git a/docs/en/compare/pp-yoloe-vs-yolov5.md b/docs/en/compare/pp-yoloe-vs-yolov5.md index ff1d860035..2cb29ea756 100644 --- a/docs/en/compare/pp-yoloe-vs-yolov5.md +++ b/docs/en/compare/pp-yoloe-vs-yolov5.md @@ -93,7 +93,7 @@ YOLOv5's versatility makes it suitable for a wide array of applications, includi ## Performance Table | Model | size
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
T4 TensorRT10
(ms) | params
(M) | FLOPs
(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 | @@ -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 diff --git a/docs/en/compare/yolo11-vs-yolov5.md b/docs/en/compare/yolo11-vs-yolov5.md index 1424aa8138..087531c9b0 100644 --- a/docs/en/compare/yolo11-vs-yolov5.md +++ b/docs/en/compare/yolo11-vs-yolov5.md @@ -90,18 +90,18 @@ YOLOv5 is widely used in applications where speed and reliability are paramount: ## Model Comparison Table | Model | size
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
T4 TensorRT10
(ms) | params
(M) | FLOPs
(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 diff --git a/docs/en/compare/yolov5-vs-yolov6.md b/docs/en/compare/yolov5-vs-yolov6.md index af034b30e3..efd504eaae 100644 --- a/docs/en/compare/yolov5-vs-yolov6.md +++ b/docs/en/compare/yolov5-vs-yolov6.md @@ -80,12 +80,12 @@ Both YOLOv5 and YOLOv6-3.0 are trained using similar methodologies common in obj ## Model Comparison Table | Model | size
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
T4 TensorRT10
(ms) | params
(M) | FLOPs
(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 | diff --git a/docs/en/compare/yolov6-vs-yolov5.md b/docs/en/compare/yolov6-vs-yolov5.md index 97f6fcb487..00d2b6e21d 100644 --- a/docs/en/compare/yolov6-vs-yolov5.md +++ b/docs/en/compare/yolov6-vs-yolov5.md @@ -87,7 +87,7 @@ YOLOv6-3.0 is designed for scenarios where high accuracy and fast inference are ## Performance Comparison Table | Model | size
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
T4 TensorRT10
(ms) | params
(M) | FLOPs
(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 | @@ -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 diff --git a/docs/en/compare/yolov7-vs-efficientdet.md b/docs/en/compare/yolov7-vs-efficientdet.md index 86fbb15f17..344e55eaf2 100644 --- a/docs/en/compare/yolov7-vs-efficientdet.md +++ b/docs/en/compare/yolov7-vs-efficientdet.md @@ -74,18 +74,18 @@ Performance metrics are crucial for evaluating object detection models. Key metr ## Model Comparison Table | Model | size
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
T4 TensorRT10
(ms) | params
(M) | FLOPs
(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 diff --git a/docs/en/compare/yolov8-vs-rtdetr.md b/docs/en/compare/yolov8-vs-rtdetr.md index f5b819ef1c..157e9b416d 100644 --- a/docs/en/compare/yolov8-vs-rtdetr.md +++ b/docs/en/compare/yolov8-vs-rtdetr.md @@ -79,10 +79,10 @@ RTDETRv2 is well-suited for applications where understanding the broader context ## Model Comparison Table | Model | size
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
T4 TensorRT10
(ms) | params
(M) | FLOPs
(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 | | | | | | | | | diff --git a/docs/en/compare/yolox-vs-efficientdet.md b/docs/en/compare/yolox-vs-efficientdet.md index 246d7e73cb..85de786774 100644 --- a/docs/en/compare/yolox-vs-efficientdet.md +++ b/docs/en/compare/yolox-vs-efficientdet.md @@ -30,7 +30,7 @@ EfficientDet focuses on achieving a balance between accuracy and efficiency thro Below is a performance comparison table for various sizes of YOLOX and EfficientDet models. | Model | size
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
T4 TensorRT10
(ms) | params
(M) | FLOPs
(B) | -| --------------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | ----- | +| --------------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | | YOLOXnano | 416 | 25.8 | - | - | 0.91 | 1.08 | | YOLOXtiny | 416 | 32.8 | - | - | 5.06 | 6.45 | | YOLOXs | 640 | 40.5 | - | 2.56 | 9.0 | 26.8 | @@ -44,7 +44,7 @@ Below is a performance comparison table for various sizes of YOLOX and Efficient | 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 | 89.29 | 51.9 | 226.0 | +| 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 | ## Performance Analysis