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yolov4 42.0% mAP.ppyolo 45.1% mAP.
| Date | Stars |
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| 2026-07-24 | 446 |
| 2026-07-25 | 446 |
| 2026-07-28 | 446 |
| 2026-07-30 | 446 |
| 2026-08-06 | 446 |
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# PPYOLO AND YOLOv4 ## 概述 PP-YOLO是[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)优化和改进的YOLOv3的模型，其精度(COCO数据集mAP)和推理速度均优于YOLOv4模型。 2020/11/05:经过不懈努力，咩酱终于在Keras上实现了可变形卷积DCNv2！这应该是咩酱最自豪的工作了。之前的种种算法（如CenterNet）因为使用了可变形卷积，而Keras、tensorflow官方没有实现可变形卷积，使得这些算法无缘在Keras平台大显身手。 而咩酱不才，一直都无法实现这一算法。经过差不多两年对算法的学习，对深度学习框架的理解，这次我再次挑战实现可变形卷积，终于大获全胜！ 值得一提的是这次的DCNv2并不需要读者编译什么c、c++、cuda、自定义op这些玩意！因为这是用tensorflow的纯python接口实现，效率极高，是咩酱的得意之作！ 带有DCNv2的PPYOLO，速度超过了不带有DCNv2的YOLOv4，咩酱也亲自与Pytorch版的PPYOLO（https://github.com/miemie2013/Pytorch-PPYOLO ）测过FPS，速度持平，可见实现的DCNv2效率极高。 其实一开始我并不想干这么费脑子的事情，但是抬头不见低头见，多造轮子其实是件好事，自己就会得到锻炼。下面我们来一览PPYOLO与YOLOv4的神采吧： | 算法 | 骨干网络 | 图片输入大小 | mAP(COCO val2017) | mAP(COCO test2017) | FPS | |:------------:|:--------:|:----:|:-------:|:-------:|:---------:| | YOLOv4 | CSPDarkNet53 | (608,608) | 0.491 | 0.420 | 10.3 | | PPYOLO | ResNet50-vd | (608,608) | 0.448 | 0.451 | 11.9 | | PPYOLO_r18vd | ResNet18-vd | (608,608) | 0.286 | - | 33.7 | | PPYOLO_r18vd | ResNet18-vd | (416,416) | 0.286 | - | 50.8 | | PPYOLO_r18vd | ResNet18-vd | (320,320) | 0.262 | - | 65.0 | **注意:** - 测速环境为： win10, i5-9400F, 8GB RAM, GTX1660Ti(6GB), cuda9, tensorflow-gpu==1.12.2。若使用Linux系统FPS还能再提高。 - FPS由demo.py测得。预测50张图片，预测之前会有一个热身(warm up)阶段使速度稳定。 - 由于原版YOLOv4使用coco trainval2014进行训练，训练样本中包含部分评估样本，若使用val2017集会导致精度虚高。所以表中的0.491的精度并不可信。 - PPYOLO使用了matrix_nms进行后处理，本仓库的YOLOv4亦使用了matrix_nms进行后处理。matrix_nms拥有和fast_nms一样的速度，mAP却比后者高。 - PPYOLO_r18vd(416,416) mAP(IoU=0.50)(COCO val2017)为0.470，表中的0.286指的是mAP(IoU=0.50:0.95)(COCO val2017)。 - PPYOLO_r18vd(608,608) mAP(IoU=0.50)(COCO val2017)为0.478。不建议使用608x608输入大小。 - PPYOLO_r18vd(320,320) mAP(IoU=0.50)(COCO val2017)为0.437。 yolov4_2x.h5在val2017下的mAP: ``` Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.491 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.720 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.546 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.331 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.554 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.610 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.357 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.599 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.650 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.450 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.719 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.803 ``` ppyolo_2x.h5在val2017下的mAP: ``` Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.448 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.649 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.492 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.265 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.483 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.593 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.337 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.571 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.624 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.420 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.665 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.773 ``` yolov4_2x.h5在test2017下的mAP: ``` Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.420 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.624 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.464 Average Precision (AP) @[ IoU=0.50:0.95 | area= s
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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