yolo26、yolo11精度对比简单记录(免费)

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yolo26、yolo11精度对比结果

yolo11

Ultralytics 8.4.8  Python-3.10.8 torch-2.3.1+cu118 CUDA:0 (NVIDIA GeForce RTX 4090, 24564MiB)
YOLO11s summary (fused): 100 layers, 9,413,187 parameters, 0 gradients, 21.3 GFLOPs
val: Fast image access  (ping: 0.00.0 ms, read: 3666.31787.2 MB/s, size: 564.4 KB)
val: Scanning 
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% ━━━━━━━━━━━━ 676/676 13.3it/s 50.8s
                   all       2704      14614      0.885      0.893      0.933      0.685
Speed: 4.5ms preprocess, 11.9ms inference, 0.0ms loss, 0.5ms postprocess per image

==================================================
small objects: 3465
medium objects: 9673
large objects: 1476
==================================================
Evaluate annotation type *bbox*
COCOeval_opt.evaluate() finished...
DONE (t=0.30s).
Accumulating evaluation results...
COCOeval_opt.accumulate() finished...
DONE (t=0.00s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.684
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.933
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.787
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.526
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.701
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.845
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.128
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.557
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.755
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.660
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.770
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.878
 Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.986
 Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.858

yolo26

Ultralytics 8.4.8  Python-3.10.8 torch-2.3.1+cu118 CUDA:0 (NVIDIA GeForce RTX 4090, 24564MiB)
YOLO26s summary (fused): 122 layers, 9,465,567 parameters, 0 gradients, 20.5 GFLOPs
val: Fast image access  (ping: 0.00.0 ms, read: 2840.01236.5 MB/s, size: 486.8 KB)
val: Scanning 
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% ━━━━━━━━━━━━ 676/676 12.4it/s 54.7s
                   all       2704      14614       0.89      0.889      0.934      0.657
Speed: 4.7ms preprocess, 13.5ms inference, 0.0ms loss, 0.1ms postprocess per image

==================================================
small objects: 3465
medium objects: 9673
large objects: 1476
==================================================
Evaluate annotation type *bbox*
COCOeval_opt.evaluate() finished...
DONE (t=0.31s).
Accumulating evaluation results...
COCOeval_opt.accumulate() finished...
DONE (t=0.00s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.656
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.933
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.783
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.517
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.679
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.781
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.122
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.536
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.735
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.642
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.753
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.826
 Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.984
 Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.865

图片结果

在这里插入图片描述

总结

其中yolo26 mAP50-95指标为0.656、yolo11 mAP50-95指标为0.684，yolo26低于yolo11
mAP50指标二者相似

其他

yolov8（Ultralytics系列）快速入门(免费)
https://blog.csdn.net/baidu_34487688/article/details/155073848

# 目标检测 # 人工智能 # 视觉检测 # 深度学习 # 神经网络
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