单目3D初始代码
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docs/en/yolov5/tutorials/test_time_augmentation.md
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---
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description: Boost your YOLOv5 performance with Test-Time Augmentation (TTA). Learn setup, testing, and inference techniques to elevate mAP and Recall.
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keywords: YOLOv5, Test-Time Augmentation, TTA, machine learning, deep learning, object detection, mAP, Recall, PyTorch
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---
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# Test-Time Augmentation (TTA)
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📚 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 🚀.
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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 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/).
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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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```text
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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_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 mAP@.5 mAP@.5:.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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## Test with TTA
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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, so ensure your GPU has enough memory headroom before increasing `--img`.
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```bash
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python val.py --weights yolov5x.pt --data coco.yaml --img 832 --augment --half
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```
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Output:
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```text
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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_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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/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.)
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return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
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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, 2885.61it/s]
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val: New cache created: ../datasets/coco/val2017.cache
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Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 157/157 [07:29<00:00, 2.86s/it]
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all 5000 36335 0.718 0.656 0.695 0.503
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Speed: 0.2ms pre-process, 80.6ms inference, 2.7ms NMS per image at shape (32, 3, 832, 832) # <--- TTA speed
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Evaluating pycocotools mAP... saving runs/val/exp2/yolov5x_predictions.json...
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...
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.516 # <--- TTA mAP
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.701
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.562
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.361
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.564
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.656
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.388
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.640
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.696 # <--- TTA mAR
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.553
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.744
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.833
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```
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## Inference with TTA
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`detect.py` TTA inference operates identically to `val.py` TTA: simply append `--augment` to any existing `detect.py` command:
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```bash
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python detect.py --weights yolov5s.pt --img 832 --source data/images --augment
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```
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Output:
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```text
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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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Downloading https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt to yolov5s.pt...
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100% 14.1M/14.1M [00:00<00:00, 81.9MB/s]
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Fusing layers...
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Model Summary: 224 layers, 7266973 parameters, 0 gradients
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image 1/2 /content/yolov5/data/images/bus.jpg: 832x640 4 persons, 1 bus, 1 fire hydrant, Done. (0.029s)
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image 2/2 /content/yolov5/data/images/zidane.jpg: 480x832 3 persons, 3 ties, Done. (0.024s)
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Results saved to runs/detect/exp
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Done. (0.156s)
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```
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<img src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/yolov5-test-time-augmentations.avif" width="500" alt="YOLOv5 test time augmentations">
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### PyTorch Hub TTA
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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.
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```python
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import torch
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# Model
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model = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5m, yolov5x, custom
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# Images
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img = "https://ultralytics.com/images/zidane.jpg" # or file, PIL, OpenCV, numpy, multiple
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# Inference
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results = model(img, augment=True) # <--- TTA inference
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# Results
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results.print() # or .show(), .save(), .crop(), .pandas(), etc.
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```
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### Customize
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You can customize the TTA operations applied in the [YOLOv5 `forward_augment()` method](https://github.com/ultralytics/yolov5/blob/8c6f9e15bfc0000d18b976a95b9d7c17d407ec91/models/yolo.py#L125-L137).
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## Benefits of Test-Time Augmentation
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Test-Time Augmentation offers several key advantages for [object detection](https://www.ultralytics.com/glossary/object-detection) tasks:
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- **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.
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- **Better Small Object Detection**: TTA particularly enhances detection of small objects, with small area AP improving from 0.351 to 0.361.
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- **Increased Robustness**: By testing multiple variations of each image, TTA reduces the impact of viewing angle, lighting, and other environmental factors.
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- **Simple Implementation**: Requires only adding the `--augment` flag to existing commands.
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The tradeoff is increased inference time, making TTA more suitable for applications where accuracy is prioritized over speed.
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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), [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/models/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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