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AGPL-3.0

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YOLOv5 CI YOLOv5 Citation Docker Pulls
Run on Gradient Open In Colab Open In Kaggle

YOLOv5 🚀 是世界上最受欢迎的视觉 AI,代表 Ultralytics 对未来视觉 AI 方法的开源研究,结合在数千小时的研究和开发中积累的经验教训和最佳实践。

我们希望这里的资源能帮助您充分利用 YOLOv5。请浏览 YOLOv5 文档 了解详细信息,在 GitHub 上提交问题以获得支持,并加入我们的 Discord 社区进行问题和讨论!

如需申请企业许可,请在 Ultralytics Licensing 处填写表格

Ultralytics GitHub Ultralytics LinkedIn Ultralytics Twitter Ultralytics YouTube Ultralytics TikTok Ultralytics Instagram Ultralytics Discord

YOLOv8 🚀 新品

我们很高兴宣布 Ultralytics YOLOv8 🚀 的发布,这是我们新推出的领先水平、最先进的(SOTA)模型,发布于 https://github.com/ultralytics/ultralytics。 YOLOv8 旨在快速、准确且易于使用,使其成为广泛的物体检测、图像分割和图像分类任务的极佳选择。

请查看 YOLOv8 文档了解详细信息,并开始使用:

PyPI 版本 下载量

pip install ultralytics

文档

有关训练、测试和部署的完整文档见YOLOv5 文档。请参阅下面的快速入门示例。

安装

克隆 repo,并要求在 Python>=3.8.0 环境中安装 requirements.txt ,且要求 PyTorch>=1.8

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

使用 YOLOv5 PyTorch Hub 推理。最新 模型 将自动的从 YOLOv5 release 中下载。

import torch

# Model
model = torch.hub.load("ultralytics/yolov5", "yolov5s")  # or yolov5n - yolov5x6, custom

# Images
img = "https://ultralytics.com/images/zidane.jpg"  # or file, Path, PIL, OpenCV, numpy, list

# Inference
results = model(img)

# Results
results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
使用 detect.py 推理

detect.py 在各种来源上运行推理, 模型 自动从 最新的YOLOv5 release 中下载,并将结果保存到 runs/detect

python detect.py --weights yolov5s.pt --source 0                               # webcam
                                               img.jpg                         # image
                                               vid.mp4                         # video
                                               screen                          # screenshot
                                               path/                           # directory
                                               list.txt                        # list of images
                                               list.streams                    # list of streams
                                               'path/*.jpg'                    # glob
                                               'https://youtu.be/LNwODJXcvt4'  # YouTube
                                               'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
训练

下面的命令重现 YOLOv5 在 COCO 数据集上的结果。 最新的 模型数据集 将自动的从 YOLOv5 release 中下载。 YOLOv5n/s/m/l/x 在 V100 GPU 的训练时间为 1/2/4/6/8 天( 多GPU 训练速度更快)。 尽可能使用更大的 --batch-size ,或通过 --batch-size -1 实现 YOLOv5 自动批处理 。下方显示的 batchsize 适用于 V100-16GB。

python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml  --batch-size 128
                                                                 yolov5s                    64
                                                                 yolov5m                    40
                                                                 yolov5l                    24
                                                                 yolov5x                    16
教程

模块集成




Roboflow ClearML ⭐ 新 Comet ⭐ 新 Neural Magic ⭐ 新
将您的自定义数据集进行标注并直接导出到 YOLOv5 以进行训练 Roboflow 自动跟踪、可视化甚至远程训练 YOLOv5 ClearML(开源!) 永远免费,Comet可让您保存 YOLOv5 模型、恢复训练以及交互式可视化和调试预测 使用 Neural Magic DeepSparse,运行 YOLOv5 推理的速度最高可提高6倍

Ultralytics HUB

Ultralytics HUB 是我们的⭐新的用于可视化数据集、训练 YOLOv5 🚀 模型并以无缝体验部署到现实世界的无代码解决方案。现在开始 免费 使用他!

为什么选择 YOLOv5

YOLOv5 超级容易上手,简单易学。我们优先考虑现实世界的结果。

YOLOv5-P5 640 图

图表笔记
  • COCO AP val 表示 mAP@0.5:0.95 指标,在 COCO val2017 数据集的 5000 张图像上测得, 图像包含 256 到 1536 各种推理大小。
  • 显卡推理速度 为在 COCO val2017 数据集上的平均推理时间,使用 AWS p3.2xlarge V100实例,batchsize 为 32 。
  • EfficientDet 数据来自 google/automl , batchsize 为32。
  • 复现命令python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt

预训练模型

模型 尺寸
(像素)
mAPval
50-95
mAPval
50
推理速度
CPU b1
(ms)
推理速度
V100 b1
(ms)
速度
V100 b32
(ms)
参数量
(M)
FLOPs
@640 (B)
YOLOv5n 640 28.0 45.7 45 6.3 0.6 1.9 4.5
YOLOv5s 640 37.4 56.8 98 6.4 0.9 7.2 16.5
YOLOv5m 640 45.4 64.1 224 8.2 1.7 21.2 49.0
YOLOv5l 640 49.0 67.3 430 10.1 2.7 46.5 109.1
YOLOv5x 640 50.7 68.9 766 12.1 4.8 86.7 205.7
YOLOv5n6 1280 36.0 54.4 153 8.1 2.1 3.2 4.6
YOLOv5s6 1280 44.8 63.7 385 8.2 3.6 12.6 16.8
YOLOv5m6 1280 51.3 69.3 887 11.1 6.8 35.7 50.0
YOLOv5l6 1280 53.7 71.3 1784 15.8 10.5 76.8 111.4
YOLOv5x6
+TTA
1280
1536
55.0
55.8
72.7
72.7
3136
-
26.2
-
19.4
-
140.7
-
209.8
-
笔记
  • 所有模型都使用默认配置,训练 300 epochs。n和s模型使用 hyp.scratch-low.yaml ,其他模型都使用 hyp.scratch-high.yaml
  • **mAPval**在单模型单尺度上计算,数据集使用 COCO val2017
    复现命令 python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65
  • 推理速度在 COCO val 图像总体时间上进行平均得到,测试环境使用AWS p3.2xlarge实例。 NMS 时间 (大约 1 ms/img) 不包括在内。
    复现命令 python val.py --data coco.yaml --img 640 --task speed --batch 1
  • TTA 测试时数据增强 包括反射和尺度变换。
    复现命令 python val.py --data coco.yaml --img 1536 --iou 0.7 --augment

实例分割模型 ⭐ 新

我们新的 YOLOv5 release v7.0 实例分割模型是世界上最快和最准确的模型,击败所有当前 SOTA 基准。我们使它非常易于训练、验证和部署。更多细节请查看 发行说明 或访问我们的 YOLOv5 分割 Colab 笔记本 以快速入门。

实例分割模型列表

我们使用 A100 GPU 在 COCO 上以 640 图像大小训练了 300 epochs 得到 YOLOv5 分割模型。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试,并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于再现,我们在 Google Colab Pro 上进行了所有速度测试。

模型 尺寸
(像素)
mAPbox
50-95
mAPmask
50-95
训练时长
300 epochs
A100 GPU(小时)
推理速度
ONNX CPU
(ms)
推理速度
TRT A100
(ms)
参数量
(M)
FLOPs
@640 (B)
YOLOv5n-seg 640 27.6 23.4 80:17 62.7 1.2 2.0 7.1
YOLOv5s-seg 640 37.6 31.7 88:16 173.3 1.4 7.6 26.4
YOLOv5m-seg 640 45.0 37.1 108:36 427.0 2.2 22.0 70.8
YOLOv5l-seg 640 49.0 39.9 66:43 (2x) 857.4 2.9 47.9 147.7
YOLOv5x-seg 640 50.7 41.4 62:56 (3x) 1579.2 4.5 88.8 265.7
  • 所有模型使用 SGD 优化器训练, 都使用 lr0=0.01weight_decay=5e-5 参数, 图像大小为 640 。
    训练 log 可以查看 https://wandb.ai/glenn-jocher/YOLOv5_v70_official
  • 准确性结果都在 COCO 数据集上,使用单模型单尺度测试得到。
    复现命令 python segment/val.py --data coco.yaml --weights yolov5s-seg.pt
  • 推理速度是使用 100 张图像推理时间进行平均得到,测试环境使用 Colab Pro 上 A100 高 RAM 实例。结果仅表示推理速度(NMS 每张图像增加约 1 毫秒)。
    复现命令 python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1
  • 模型转换到 FP32 的 ONNX 和 FP16 的 TensorRT 脚本为 export.py.
    运行命令 python export.py --weights yolov5s-seg.pt --include engine --device 0 --half
分割模型使用示例  Open In Colab

训练

YOLOv5分割训练支持自动下载 COCO128-seg 分割数据集,用户仅需在启动指令中包含 --data coco128-seg.yaml 参数。 若要手动下载,使用命令 bash data/scripts/get_coco.sh --train --val --segments, 在下载完毕后,使用命令 python train.py --data coco.yaml 开启训练。

# 单 GPU
python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640

# 多 GPU, DDP 模式
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3

验证

在 COCO 数据集上验证 YOLOv5s-seg mask mAP:

bash data/scripts/get_coco.sh --val --segments  # 下载 COCO val segments 数据集 (780MB, 5000 images)
python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640  # 验证

预测

使用预训练的 YOLOv5m-seg.pt 来预测 bus.jpg:

python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg
model = torch.hub.load(
    "ultralytics/yolov5", "custom", "yolov5m-seg.pt"
)  # 从load from PyTorch Hub 加载模型 (WARNING: 推理暂未支持)
zidane bus

模型导出

将 YOLOv5s-seg 模型导出到 ONNX 和 TensorRT:

python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0

分类网络 ⭐ 新

YOLOv5 release v6.2 带来对分类模型训练、验证和部署的支持!详情请查看 发行说明 或访问我们的 YOLOv5 分类 Colab 笔记本 以快速入门。

分类网络模型

我们使用 4xA100 实例在 ImageNet 上训练了 90 个 epochs 得到 YOLOv5-cls 分类模型,我们训练了 ResNet 和 EfficientNet 模型以及相同的默认训练设置以进行比较。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试,并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于重现,我们在 Google 上进行了所有速度测试 Colab Pro

模型 尺寸
(像素)
acc
top1
acc
top5
训练时长
90 epochs
4xA100(小时)
推理速度
ONNX CPU
(ms)
推理速度
TensorRT V100
(ms)
参数
(M)
FLOPs
@640 (B)
YOLOv5n-cls 224 64.6 85.4 7:59 3.3 0.5 2.5 0.5
YOLOv5s-cls 224 71.5 90.2 8:09 6.6 0.6 5.4 1.4
YOLOv5m-cls 224 75.9 92.9 10:06 15.5 0.9 12.9 3.9
YOLOv5l-cls 224 78.0 94.0 11:56 26.9 1.4 26.5 8.5
YOLOv5x-cls 224 79.0 94.4 15:04 54.3 1.8 48.1 15.9
ResNet18 224 70.3 89.5 6:47 11.2 0.5 11.7 3.7
Resnetzch 224 73.9 91.8 8:33 20.6 0.9 21.8 7.4
ResNet50 224 76.8 93.4 11:10 23.4 1.0 25.6 8.5
ResNet101 224 78.5 94.3 17:10 42.1 1.9 44.5 15.9
EfficientNet_b0 224 75.1 92.4 13:03 12.5 1.3 5.3 1.0
EfficientNet_b1 224 76.4 93.2 17:04 14.9 1.6 7.8 1.5
EfficientNet_b2 224 76.6 93.4 17:10 15.9 1.6 9.1 1.7
EfficientNet_b3 224 77.7 94.0 19:19 18.9 1.9 12.2 2.4
Table Notes (点击以展开)
  • 所有模型都使用 SGD 优化器训练 90 个 epochs,都使用 lr0=0.001weight_decay=5e-5 参数, 图像大小为 224 ,且都使用默认设置。
    训练 log 可以查看 https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2
  • 准确性都在单模型单尺度上计算,数据集使用 ImageNet-1k
    复现命令 python classify/val.py --data ../datasets/imagenet --img 224
  • 推理速度是使用 100 个推理图像进行平均得到,测试环境使用谷歌 Colab Pro V100 高 RAM 实例。
    复现命令 python classify/val.py --data ../datasets/imagenet --img 224 --batch 1
  • 模型导出到 FP32 的 ONNX 和 FP16 的 TensorRT 使用 export.py
    复现命令 python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224
分类训练示例  Open In Colab

训练

YOLOv5 分类训练支持自动下载 MNIST、Fashion-MNIST、CIFAR10、CIFAR100、Imagenette、Imagewoof 和 ImageNet 数据集,命令中使用 --data 即可。 MNIST 示例 --data mnist

# 单 GPU
python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128

# 多 GPU, DDP 模式
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3

验证

在 ImageNet-1k 数据集上验证 YOLOv5m-cls 的准确性:

bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)
python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate

预测

使用预训练的 YOLOv5s-cls.pt 来预测 bus.jpg:

python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg
model = torch.hub.load(
    "ultralytics/yolov5", "custom", "yolov5s-cls.pt"
)  # load from PyTorch Hub

模型导出

将一组经过训练的 YOLOv5s-cls、ResNet 和 EfficientNet 模型导出到 ONNX 和 TensorRT:

python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224

环境

使用下面我们经过验证的环境,在几秒钟内开始使用 YOLOv5 。单击下面的图标了解详细信息。

贡献

我们喜欢您的意见或建议!我们希望尽可能简单和透明地为 YOLOv5 做出贡献。请看我们的 投稿指南,并填写 YOLOv5调查 向我们发送您的体验反馈。感谢我们所有的贡献者!

许可证

Ultralytics 提供两种许可证选项以适应各种使用场景:

  • AGPL-3.0 许可证:这个OSI 批准的开源许可证非常适合学生和爱好者,可以推动开放的协作和知识分享。请查看LICENSE 文件以了解更多细节。
  • 企业许可证:专为商业用途设计,该许可证允许将 Ultralytics 的软件和 AI 模型无缝集成到商业产品和服务中,从而绕过 AGPL-3.0 的开源要求。如果您的场景涉及将我们的解决方案嵌入到商业产品中,请通过 Ultralytics Licensing与我们联系。

联系方式

对于 Ultralytics 的错误报告和功能请求,请访问 GitHub Issues,并加入我们的 Discord 社区进行问题和讨论!


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