Link to this sectionYOLOv5 模型集成#

📚 本指南介绍了如何在测试和推理过程中使用 Ultralytics YOLOv5 🚀 模型集成 (model ensembling)，以提升 mAP 和 Recall 指标。

集成建模是一个通过创建多个不同模型来预测结果的过程，这些模型可以通过使用许多不同的建模算法或使用不同的训练数据集来创建。集成模型随后会汇总每个基模型的预测结果，从而针对未知数据给出一个最终预测。使用集成模型的初衷是为了减少预测的泛化误差。只要基模型具有多样性且相互独立，使用集成方法后模型的预测误差就会降低。这种方法旨在通过“群体智慧”来进行预测。尽管集成模型内部包含多个基模型，但其运行和表现方式就像单个模型一样。

Link to this section开始之前#

Clone repo and install requirements.txt in a Python>=3.8.0 environment, including PyTorch>=1.8. Models and datasets download automatically from the latest YOLOv5 release.

git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install

Before ensembling, establish the baseline performance of a single model. This command tests YOLOv5x on COCO val2017 at image size 640 pixels. yolov5x.pt is the largest and most accurate model available. Other options are yolov5s.pt, yolov5m.pt and yolov5l.pt, or your own checkpoint from training a custom dataset ./weights/best.pt. For details on all available models, see the pretrained checkpoints table.

python val.py --weights yolov5x.pt --data coco.yaml --img 640 --half

输出：

val: data=./data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, single_cls=False, augment=False, verbose=False, save_txt=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True
YOLOv5 🚀 v5.0-267-g6a3ee7c torch 1.9.0+cu102 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)

Fusing layers...
Model Summary: 476 layers, 87730285 parameters, 0 gradients

val: Scanning '../datasets/coco/val2017' images and labels...4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 2846.03it/s]
val: New cache created: ../datasets/coco/val2017.cache
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 157/157 [02:30<00:00,  1.05it/s]
                 all       5000      36335      0.746      0.626       0.68       0.49
Speed: 0.1ms pre-process, 22.4ms inference, 1.4ms NMS per image at shape (32, 3, 640, 640)  # <--- baseline speed

Evaluating pycocotools mAP... saving runs/val/exp/yolov5x_predictions.json...
...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.504  # <--- baseline mAP
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.688
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.546
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.351
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.551
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.644
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.382
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.628
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.681  # <--- baseline mAR
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.524
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.735
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.826

Link to this section集成测试#

在测试和推理时，只需在现有的任何 val.py 或 detect.py 命令中向 --weights 参数追加额外的模型，即可将多个预训练模型集成在一起。此示例展示了如何同时测试两个模型的集成：

YOLOv5x
YOLOv5l6

python val.py --weights yolov5x.pt yolov5l6.pt --data coco.yaml --img 640 --half

你可以根据需要列出任意数量的检查点，包括自定义权重，例如 runs/train/exp-5/weights/best.pt。YOLOv5 会自动运行每个模型，在单张图像的基础上对齐预测结果，并在执行 NMS 之前对输出进行平均处理。

输出：

val: data=./data/coco.yaml, weights=['yolov5x.pt', 'yolov5l6.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, task=val, device=, single_cls=False, augment=False, verbose=False, save_txt=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True
YOLOv5 🚀 v5.0-267-g6a3ee7c torch 1.9.0+cu102 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)

Fusing layers...
Model Summary: 476 layers, 87730285 parameters, 0 gradients  # Model 1
Fusing layers...
Model Summary: 501 layers, 77218620 parameters, 0 gradients  # Model 2
Ensemble created with ['yolov5x.pt', 'yolov5l6.pt']  # Ensemble notice

val: Scanning '../datasets/coco/val2017.cache' images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:00<00:00, 49695545.02it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 157/157 [03:58<00:00,  1.52s/it]
                 all       5000      36335      0.747      0.637      0.692      0.502
Speed: 0.1ms pre-process, 39.5ms inference, 2.0ms NMS per image at shape (32, 3, 640, 640)  # <--- ensemble speed

Evaluating pycocotools mAP... saving runs/val/exp-3/yolov5x_predictions.json...
...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.515  # <--- ensemble mAP
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.699
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.557
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.356
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.563
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.668
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.387
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.638
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.689  # <--- ensemble mAR
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.526
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.743
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.844

Link to this section集成推理#

向 --weights 参数追加额外模型即可运行集成推理：

python detect.py --weights yolov5x.pt yolov5l6.pt --img 640 --source data/images

输出：

YOLOv5 🚀 v5.0-267-g6a3ee7c torch 1.9.0+cu102 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)

Fusing layers...
Model Summary: 476 layers, 87730285 parameters, 0 gradients
Fusing layers...
Model Summary: 501 layers, 77218620 parameters, 0 gradients
Ensemble created with ['yolov5x.pt', 'yolov5l6.pt']

image 1/2 /content/yolov5/data/images/bus.jpg: 640x512 4 persons, 1 bus, 1 tie, Done. (0.063s)
image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 3 persons, 2 ties, Done. (0.056s)
Results saved to runs/detect/exp-2
Done. (0.223s)

Link to this section模型集成的优势#

使用 YOLOv5 进行模型集成具有以下几个优势：

更高的准确度：如上例所示，集成多个模型可将 mAP 从 0.504 提升至 0.515，并将 mAR 从 0.681 提升至 0.689。
更好的泛化能力：结合多种不同的模型有助于减少过拟合，并提高在各类数据上的表现。
更强的鲁棒性：集成模型通常对数据中的噪声和异常值具有更强的鲁棒性。
互补优势：不同的模型可能擅长检测不同类型的对象或在不同的环境条件下表现更好。

主要的权衡是增加了推理时间，如速度指标所示（单个模型为 22.4ms，而集成模型为 39.5ms）。

Link to this section何时使用模型集成#

在以下场景中考虑使用模型集成：

当准确度比推理速度更重要时
对于必须最小化假阴性的关键应用场景
处理光照、遮挡或尺度各异的复杂图像时
在需要最高性能的竞赛或基准测试中

对于有严格延迟要求的实时应用，使用单个模型进行推理可能更合适。

Link to this section支持的环境#

Ultralytics 提供了一系列即用型环境，预装了 CUDA、CUDNN、Python 和 PyTorch 等关键依赖，助你快速启动项目。

免费 GPU 笔记本：
Google Cloud: GCP 快速入门指南
Amazon: AWS 快速入门指南
Azure: AzureML 快速入门指南
Docker: Docker 快速入门指南

Link to this section项目状态#

此徽章表示所有 YOLOv5 GitHub Actions 持续集成 (CI) 测试均已成功通过。这些 CI 测试严格检查 YOLOv5 在训练、验证、推理、导出和基准测试等各个关键方面的功能和性能。它们确保了在 macOS、Windows 和 Ubuntu 上的稳定可靠运行，测试每 24 小时进行一次，并会在每次新提交代码时自动触发。

贡献者

RAraimbekovm¹ GLglenn-jocher¹

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