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---
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comments: true
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description: Learn how to use YOLOv5 model ensembling during testing and inference to enhance mAP and Recall for more accurate predictions.
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keywords: YOLOv5, model ensembling, testing, inference, mAP, Recall, Ultralytics, object detection, PyTorch
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---
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📚 This guide explains how to use YOLOv5 🚀 **model ensembling** during testing and inference for improved mAP and [Recall](https://www.ultralytics.com/glossary/recall).
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From [https://en.wikipedia.org/wiki/Ensemble_learning](https://en.wikipedia.org/wiki/Ensemble_learning):
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> Ensemble modeling is a process where multiple diverse models are created to predict an outcome, either by using many different modeling algorithms or using different [training data](https://www.ultralytics.com/glossary/training-data) sets. The ensemble model then aggregates the prediction of each base model and results in once final prediction for the unseen data. The motivation for using ensemble models is to reduce the generalization error of the prediction. As long as the base models are diverse and independent, the prediction error of the model decreases when the ensemble approach is used. The approach seeks the wisdom of crowds in making a prediction. Even though the ensemble model has multiple base models within the model, it acts and performs as a single model.
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## Before You Start
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Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.8.0**](https://www.python.org/) environment, including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
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```bash
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git clone https://github.com/ultralytics/yolov5 # clone
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cd yolov5
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pip install -r requirements.txt # install
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```
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## Test Normally
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Before ensembling we want to 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 you own checkpoint from training a custom dataset `./weights/best.pt`. For details on all available models please see our README [table](https://github.com/ultralytics/yolov5#pretrained-checkpoints).
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```bash
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python val.py --weights yolov5x.pt --data coco.yaml --img 640 --half
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```
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Output:
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```shell
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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_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True
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YOLOv5 🚀 v5.0-267-g6a3ee7c torch 1.9.0+cu102 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)
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Fusing layers...
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Model Summary: 476 layers, 87730285 parameters, 0 gradients
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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]
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val: New cache created: ../datasets/coco/val2017.cache
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Class Images Labels P R [email protected] [email protected]:.95: 100% 157/157 [02:30<00:00, 1.05it/s]
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all 5000 36335 0.746 0.626 0.68 0.49
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Speed: 0.1ms pre-process, 22.4ms inference, 1.4ms NMS per image at shape (32, 3, 640, 640) # <--- baseline speed
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Evaluating pycocotools mAP... saving runs/val/exp/yolov5x_predictions.json...
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...
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.504 # <--- baseline mAP
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.688
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.546
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.351
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.551
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.644
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.382
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.628
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.681 # <--- baseline mAR
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.524
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.735
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.826
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```
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## Ensemble Test
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Multiple pretrained models may be ensembled together at test and inference time by simply appending extra models to the `--weights` argument in any existing val.py or detect.py command. This example tests an ensemble of 2 models together:
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- YOLOv5x
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- YOLOv5l6
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```bash
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python val.py --weights yolov5x.pt yolov5l6.pt --data coco.yaml --img 640 --half
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```
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Output:
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```shell
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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_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True
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YOLOv5 🚀 v5.0-267-g6a3ee7c torch 1.9.0+cu102 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)
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Fusing layers...
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Model Summary: 476 layers, 87730285 parameters, 0 gradients # Model 1
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Fusing layers...
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Model Summary: 501 layers, 77218620 parameters, 0 gradients # Model 2
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Ensemble created with ['yolov5x.pt', 'yolov5l6.pt'] # Ensemble notice
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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]
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Class Images Labels P R [email protected] [email protected]:.95: 100% 157/157 [03:58<00:00, 1.52s/it]
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all 5000 36335 0.747 0.637 0.692 0.502
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Speed: 0.1ms pre-process, 39.5ms inference, 2.0ms NMS per image at shape (32, 3, 640, 640) # <--- ensemble speed
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Evaluating pycocotools mAP... saving runs/val/exp3/yolov5x_predictions.json...
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...
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.515 # <--- ensemble mAP
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.699
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.557
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.356
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.563
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.668
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.387
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.638
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.689 # <--- ensemble mAR
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.526
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.743
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.844
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```
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## Ensemble Inference
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Append extra models to the `--weights` argument to run ensemble inference:
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```bash
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python detect.py --weights yolov5x.pt yolov5l6.pt --img 640 --source data/images
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```
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Output:
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```bash
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YOLOv5 🚀 v5.0-267-g6a3ee7c torch 1.9.0+cu102 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)
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Fusing layers...
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Model Summary: 476 layers, 87730285 parameters, 0 gradients
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Fusing layers...
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Model Summary: 501 layers, 77218620 parameters, 0 gradients
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Ensemble created with ['yolov5x.pt', 'yolov5l6.pt']
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image 1/2 /content/yolov5/data/images/bus.jpg: 640x512 4 persons, 1 bus, 1 tie, Done. (0.063s)
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image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 3 persons, 2 ties, Done. (0.056s)
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Results saved to runs/detect/exp2
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Done. (0.223s)
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```
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<img src="https://github.com/ultralytics/docs/releases/download/0/yolo-inference-result.avif" width="500" alt="YOLO inference result">
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## Supported Environments
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Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
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- **Free GPU Notebooks**: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
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- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)
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- **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md)
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- **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md)
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- **Docker**: [Docker Quickstart Guide](../environments/docker_image_quickstart_tutorial.md) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
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## Project Status
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<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
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This badge indicates that all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are successfully passing. These CI tests rigorously check the functionality and performance of YOLOv5 across various key aspects: [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py), and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py). They ensure consistent and reliable operation on macOS, Windows, and Ubuntu, with tests conducted every 24 hours and upon each new commit.
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