--- comments: true description: Boost your YOLOv5 performance with Test-Time Augmentation (TTA). Learn setup, testing, and inference techniques to elevate mAP and Recall. keywords: YOLOv5, Test-Time Augmentation, TTA, machine learning, deep learning, object detection, mAP, Recall, PyTorch --- # Test-Time Augmentation (TTA) 📚 This guide explains how to use Test Time Augmentation (TTA) during testing and inference for improved mAP and [Recall](https://www.ultralytics.com/glossary/recall) with YOLOv5 🚀. ## Before You Start 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). ```bash git clone https://github.com/ultralytics/yolov5 # clone cd yolov5 pip install -r requirements.txt # install ``` ## Test Normally Before trying TTA we want to establish a baseline performance to compare to. 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 please see our [YOLOv5 documentation](https://docs.ultralytics.com/models/yolov5/). ```bash python val.py --weights yolov5x.pt --data coco.yaml --img 640 --half ``` Output: ```shell 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 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 ``` ## Test with TTA Append `--augment` to any existing `val.py` command to enable TTA, and increase the image size by about 30% for improved results. Note that inference with TTA enabled will typically take about 2-3X the time of normal inference as the images are being left-right flipped and processed at 3 different resolutions, with the outputs merged before [NMS](https://www.ultralytics.com/glossary/non-maximum-suppression-nms). Part of the speed decrease is simply due to larger image sizes (832 vs 640), while part is due to the actual TTA operations. ```bash python val.py --weights yolov5x.pt --data coco.yaml --img 832 --augment --half ``` Output: ```shell val: data=./data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=832, conf_thres=0.001, iou_thres=0.6, task=val, device=, single_cls=False, augment=True, verbose=False, save_txt=False, save_hybrid=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... /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.) return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) 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, 2885.61it/s] val: New cache created: ../datasets/coco/val2017.cache Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 157/157 [07:29<00:00, 2.86s/it] all 5000 36335 0.718 0.656 0.695 0.503 Speed: 0.2ms pre-process, 80.6ms inference, 2.7ms NMS per image at shape (32, 3, 832, 832) # <--- TTA speed Evaluating pycocotools mAP... saving runs/val/exp2/yolov5x_predictions.json... ... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.516 # <--- TTA mAP Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.701 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.562 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.361 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.564 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.656 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.388 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.640 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.696 # <--- TTA mAR Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.553 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.744 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.833 ``` ## Inference with TTA `detect.py` TTA inference operates identically to `val.py` TTA: simply append `--augment` to any existing `detect.py` command: ```bash python detect.py --weights yolov5s.pt --img 832 --source data/images --augment ``` Output: ```bash YOLOv5 🚀 v5.0-267-g6a3ee7c torch 1.9.0+cu102 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB) Downloading https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt to yolov5s.pt... 100% 14.1M/14.1M [00:00<00:00, 81.9MB/s] Fusing layers... Model Summary: 224 layers, 7266973 parameters, 0 gradients image 1/2 /content/yolov5/data/images/bus.jpg: 832x640 4 persons, 1 bus, 1 fire hydrant, Done. (0.029s) image 2/2 /content/yolov5/data/images/zidane.jpg: 480x832 3 persons, 3 ties, Done. (0.024s) Results saved to runs/detect/exp Done. (0.156s) ``` YOLOv5 test time augmentations ### PyTorch Hub TTA TTA is automatically integrated into all [YOLOv5 PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/) models, and can be accessed by passing `augment=True` at inference time. ```python import torch # Model model = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5m, yolov5x, custom # Images img = "https://ultralytics.com/images/zidane.jpg" # or file, PIL, OpenCV, numpy, multiple # Inference results = model(img, augment=True) # <--- TTA inference # Results results.print() # or .show(), .save(), .crop(), .pandas(), etc. ``` ### Customize You can customize the TTA operations applied in the YOLOv5 `forward_augment()` method [here](https://github.com/ultralytics/yolov5/blob/8c6f9e15bfc0000d18b976a95b9d7c17d407ec91/models/yolo.py#L125-L137). ## Benefits of Test-Time Augmentation Test-Time Augmentation offers several key advantages for [object detection](https://www.ultralytics.com/glossary/object-detection) tasks: - **Improved Accuracy**: As demonstrated in the results above, TTA increases mAP from 0.504 to 0.516 and mAR from 0.681 to 0.696. - **Better Small Object Detection**: TTA particularly enhances detection of small objects, with small area AP improving from 0.351 to 0.361. - **Increased Robustness**: By testing multiple variations of each image, TTA reduces the impact of viewing angle, lighting, and other environmental factors. - **Simple Implementation**: Requires only adding the `--augment` flag to existing commands. The tradeoff is increased inference time, making TTA more suitable for applications where accuracy is prioritized over speed. ## Supported Environments 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. - **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle - **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md) - **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md) - **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md) - **Docker**: [Docker Quickstart Guide](../environments/docker_image_quickstart_tutorial.md) Docker Pulls ## Project Status YOLOv5 CI 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.