ml-project-object-detection-for-autonomous-vehicles

https://github.com/dheerajreddy258/ml-project-object-detection-for-autonomous-vehicles

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Language: Python
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Created about 3 years ago · Last pushed about 3 years ago

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Readme Contributing License Code of conduct Citation Security

.github/README_cn.md

[English](../README.md) | 简体中文

YOLOv5🚀是一个在COCO数据集上预训练的物体检测架构和模型系列，它代表了Ultralytics对未来视觉AI方法的公开研究，其中包含了在数千小时的研究和开发中所获得的经验和最佳实践。

文件

请参阅YOLOv5 Docs，了解有关训练、测试和部署的完整文件。

快速开始案例

安装

在[**Python>=3.7.0**](https://www.python.org/) 的环境中克隆版本仓并安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt)，包括[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/)。 ```bash git clone https://github.com/ultralytics/yolov5 # 克隆 cd yolov5 pip install -r requirements.txt # 安装 ```

推理

YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 推理. [模型](https://github.com/ultralytics/yolov5/tree/master/models) 自动从最新YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases)下载。 ```python import torch # 模型 model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5n - yolov5x6, custom # 图像 img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list # 推理 results = model(img) # 结果 results.print() # or .show(), .save(), .crop(), .pandas(), etc. ```

用 detect.py 进行推理

`detect.py` 在各种数据源上运行推理, 其会从最新的 YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases) 中自动下载 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 并将检测结果保存到 `runs/detect` 目录。 ```bash python detect.py --source 0 # 网络摄像头 img.jpg # 图像 vid.mp4 # 视频 path/ # 文件夹 'path/*.jpg' # glob 'https://youtu.be/Zgi9g1ksQHc' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP 流 ```

训练

以下指令再现了 YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) 数据集结果. [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data) 自动从最新的YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases) 中下载。YOLOv5n/s/m/l/x的训练时间在V100 GPU上是 1/2/4/6/8天（多GPU倍速）. 尽可能使用最大的 `--batch-size`, 或通过 `--batch-size -1` 来实现 YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092). 批量大小显示为 V100-16GB。 ```bash 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

- [训练自定义数据集](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) 🚀 推荐 - [获得最佳训练效果的技巧](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) ☘️ 推荐 - [多GPU训练](https://github.com/ultralytics/yolov5/issues/475) - [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 🌟 新 - [TFLite, ONNX, CoreML, TensorRT 输出](https://github.com/ultralytics/yolov5/issues/251) 🚀 - [测试时数据增强 (TTA)](https://github.com/ultralytics/yolov5/issues/303) - [模型集成](https://github.com/ultralytics/yolov5/issues/318) - [模型剪枝/稀疏性](https://github.com/ultralytics/yolov5/issues/304) - [超参数进化](https://github.com/ultralytics/yolov5/issues/607) - [带有冻结层的迁移学习](https://github.com/ultralytics/yolov5/issues/1314) - [架构概要](https://github.com/ultralytics/yolov5/issues/6998) 🌟 新 - [使用Weights & Biases 记录实验](https://github.com/ultralytics/yolov5/issues/1289) - [Roboflow：数据集，标签和主动学习](https://github.com/ultralytics/yolov5/issues/4975) 🌟 新 - [使用ClearML 记录实验](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) 🌟 新 - [Deci 平台](https://github.com/ultralytics/yolov5/wiki/Deci-Platform) 🌟 新

Integrations

Ultralytics HUB

Ultralytics HUB is our ⭐ NEW no-code solution to visualize datasets, train YOLOv5 🚀 models, and deploy to the real world in a seamless experience. Get started for Free now!

为什么选择 YOLOv5

YOLOv5-P5 640 图像 (点击扩展)

图片注释 (点击扩展)

- **COCO AP val** 表示 mAP@0.5:0.95 在5000张图像的[COCO val2017](http://cocodataset.org)数据集上，在256到1536的不同推理大小上测量的指标。 - **GPU Speed** 衡量的是在 [COCO val2017](http://cocodataset.org) 数据集上使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100实例在批量大小为32时每张图像的平均推理时间。 - **EfficientDet** 数据来自 [google/automl](https://github.com/google/automl) ，批量大小设置为 8。 - 复现 mAP 方法: `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`

预训练检查点

| 模型 | 规模
^{(像素) | mAP^{验证
0.5:0.95 | mAP^{验证
0.5 | 速度
^{CPU b1
(ms) | 速度
^{V100 b1
(ms) | 速度
^{V100 b32
(ms) | 参数
^{(M) | 浮点运算
^{@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
- |}}}}}}}}

表格注释 (点击扩展)

- 所有检查点都以默认设置训练到300个时期. Nano和Small模型用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, 其他模型使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml). - **mAP^val** 值是 [COCO val2017](http://cocodataset.org) 数据集上的单模型单尺度的值。
复现方法: `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` - 使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) 实例对COCO val图像的平均速度。不包括NMS时间（~1 ms/img)
复现方法: `python val.py --data coco.yaml --img 640 --task speed --batch 1` - **TTA** [测试时数据增强](https://github.com/ultralytics/yolov5/issues/303) 包括反射和比例增强.
复现方法: `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`

分类 ⭐ 新

YOLOv5发布的v6.2版本支持训练，验证，预测和输出分类模型！这使得训练分类器模型非常简单。点击下面开始尝试！

分类检查点 (点击展开)

我们在ImageNet上使用了4xA100的实例训练YOLOv5-cls分类模型90个epochs，并以相同的默认设置同时训练了ResNet和EfficientNet模型来进行比较。我们将所有的模型导出到ONNX FP32进行CPU速度测试，又导出到TensorRT FP16进行GPU速度测试。最后，为了方便重现，我们在[Google Colab Pro](https://colab.research.google.com/signup)上进行了所有的速度测试。 | 模型 | 规模
^{(像素) | 准确度
^{第一 | 准确度
^{前五 | 训练
^{90 epochs
4xA100 (小时) | 速度
^{ONNX CPU
(ms) | 速度
^{TensorRT V100
(ms) | 参数
^{(M) | 浮点运算
^{@224 (B) |
|----------------------------------------------------------------------------------------------------|-----------------------|------------------|------------------|----------------------------------------------|--------------------------------|-------------------------------------|--------------------|------------------------|
| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** |
| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 |
| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 |
| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 |
| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 |
| |
| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 |
| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 |
| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 |
| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 |
| |
| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 |
| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 |
| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 |
| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 |

表格注释 (点击扩展)

- 所有检查点都被SGD优化器训练到90 epochs, `lr0=0.001` 和 `weight_decay=5e-5`，图像大小为224，全为默认设置。
运行数据记录于 https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2。
- **准确度** 值为[ImageNet-1k](https://www.image-net.org/index.php)数据集上的单模型单尺度。
通过`python classify/val.py --data ../datasets/imagenet --img 224`进行复制。
- 使用Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM实例得出的100张推理图像的平均**速度**。
通过 `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`进行复制。
- 用`export.py`**导出**到FP32的ONNX和FP16的TensorRT。
通过 `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`进行复制。}}}}}}}}

分类使用实例 (点击展开)

### 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，并使用`--data` 参数. 打个比方，在MNIST上使用`--data mnist`开始训练。 ```bash # 单GPU python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128 # 多-GPU DDP 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-cl的准确性: ```bash 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 ``` ### 预测用提前训练好的YOLOv5s-cls.pt去预测bus.jpg: ```bash python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg ``` ```python model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s-cls.pt') # load from PyTorch Hub ``` ### 导出导出一组训练好的YOLOv5s-cls, ResNet和EfficientNet模型到ONNX和TensorRT: ```bash python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224 ```

贡献

我们重视您的意见! 我们希望给大家提供尽可能的简单和透明的方式对 YOLOv5 做出贡献。开始之前请先点击并查看我们的贡献指南，填写YOLOv5调查问卷来向我们发送您的经验反馈。真诚感谢我们所有的贡献者!

联系

关于YOLOv5的漏洞和功能问题，请访问 GitHub Issues。商业咨询或技术支持服务请访问https://ultralytics.com/contact。

Owner

Name: Pullela Dheeraj Reddy
Login: dheerajreddy258
Kind: user

Repositories: 1
Profile: https://github.com/dheerajreddy258

Citation (CITATION.cff)

cff-version: 1.2.0
preferred-citation:
  type: software
  message: If you use YOLOv5, please cite it as below.
  authors:
  - family-names: Jocher
    given-names: Glenn
    orcid: "https://orcid.org/0000-0001-5950-6979"
  title: "YOLOv5 by Ultralytics"
  version: 7.0
  doi: 10.5281/zenodo.3908559
  date-released: 2020-5-29
  license: GPL-3.0
  url: "https://github.com/ultralytics/yolov5"

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