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<img width="800" src="https://user-images.githubusercontent.com/26833433/98699617-a1595a00-2377-11eb-8145-fc674eb9b1a7.jpg"></a>
 
<a href="https://github.com/ultralytics/yolov5/actions"><img src="https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a>
This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices evolved over thousands of hours of training and evolution on anonymized client datasets. **All code and models are under active development, and are subject to modification or deletion without notice.** Use at your own risk.
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313216-f0a5e100-9af5-11eb-8445-c682b60da2e3.png"></p>
<details>
<summary>YOLOv5-P5 640 Figure (click to expand)</summary>
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313219-f1d70e00-9af5-11eb-9973-52b1f98d321a.png"></p>
</details>
<details>
<summary>Figure Notes (click to expand)</summary>
* GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
* EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8.
* **Reproduce** by `python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
</details>
- **April 11, 2021**: [v5.0 release](https://github.com/ultralytics/yolov5/releases/tag/v5.0): YOLOv5-P6 1280 models, [AWS](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart), [Supervise.ly](https://github.com/ultralytics/yolov5/issues/2518) and [YouTube](https://github.com/ultralytics/yolov5/pull/2752) integrations.
- **January 5, 2021**: [v4.0 release](https://github.com/ultralytics/yolov5/releases/tag/v4.0): nn.SiLU() activations, [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme) logging, [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/) integration.
- **August 13, 2020**: [v3.0 release](https://github.com/ultralytics/yolov5/releases/tag/v3.0): nn.Hardswish() activations, data autodownload, native AMP.
- **July 23, 2020**: [v2.0 release](https://github.com/ultralytics/yolov5/releases/tag/v2.0): improved model definition, training and mAP.
## Pretrained Checkpoints
[assets]: https://github.com/ultralytics/yolov5/releases
Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>V100 (ms) | |params<br><sup>(M) |FLOPS<br><sup>640 (B)
--- |--- |--- |--- |--- |--- |---|--- |---
[YOLOv5s][assets] |640 |36.7 |36.7 |55.4 |**2.0** | |7.3 |17.0
[YOLOv5m][assets] |640 |44.5 |44.5 |63.1 |2.7 | |21.4 |51.3
[YOLOv5l][assets] |640 |48.2 |48.2 |66.9 |3.8 | |47.0 |115.4
[YOLOv5x][assets] |640 |**50.4** |**50.4** |**68.8** |6.1 | |87.7 |218.8
| | | | | | || |
[YOLOv5s6][assets] |1280 |43.3 |43.3 |61.9 |**4.3** | |12.7 |17.4
[YOLOv5m6][assets] |1280 |50.5 |50.5 |68.7 |8.4 | |35.9 |52.4
[YOLOv5l6][assets] |1280 |53.4 |53.4 |71.1 |12.3 | |77.2 |117.7
[YOLOv5x6][assets] |1280 |**54.4** |**54.4** |**72.0** |22.4 | |141.8 |222.9
| | | | | | || |
[YOLOv5x6][assets] TTA |1280 |**55.0** |**55.0** |**72.0** |70.8 | |- |-
<details>
<summary>Table Notes (click to expand)</summary>
* AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy.
* AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
* Speed<sub>GPU</sub> averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes FP16 inference, postprocessing and NMS. **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`
* All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
* Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) includes reflection and scale augmentation. **Reproduce TTA** by `python test.py --data coco.yaml --img 1536 --iou 0.7 --augment`
</details>
## Requirements
Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed, including `torch>=1.7`. To install run:
<!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev -->
```bash
$ pip install -r requirements.txt
```
## Tutorials
* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) ð RECOMMENDED
* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) âï¸ RECOMMENDED
* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289) ð NEW
* [Supervisely Ecosystem](https://github.com/ultralytics/yolov5/issues/2518) ð NEW
* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) â NEW
* [ONNX and TorchScript Export](https://github.com/ultralytics/yolov5/issues/251)
* [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
* [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
* [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
* [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
* [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314) â NEW
* [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx)
## Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
- **Google Colab and Kaggle** notebooks with free GPU: <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>
- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <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>
## Inference
`detect.py` runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
```bash
$ python detect.py --source 0 # webcam
file.jpg # image
file.mp4 # video
path/ # directory
path/*.jpg # glob
'https://youtu.be/NUsoVlDFqZg' # YouTube video
'rtsp://example.com/media.mp4' # RTSP, RTM
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基于Yolov5-DeepSort的物体计数器,可以统计车流或人流量等
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【作品名称】:基于Yolov5_DeepSort的物体计数器,可以统计车流或人流量等 【适用人群】:适用于希望学习不同技术领域的小白或进阶学习者。可作为毕设项目、课程设计、大作业、工程实训或初期项目立项。 【项目介绍】:检测原理 Yolov5_DeepSort会跟踪画面上检测出来的物体,并给每个框标上了序号,当有一个方框跨过检测线时,计数器就会+1 用户可以定义多条检测线,也可以指定框的四个顶点或中心点哪一个作为检测点 具体的参数设定见第3点 3、参数设置 在count.py中,设置以下参数 source_dir : 要打开的视频文件。若要调用摄像头,需要设置为字符串'0',而不是数字0,按q退出播放 output_dir : 要保存到的文件夹 show_video : 运行时是否显示 save_video : 是否保存运行结果视频 save_text : 是否保存结果数据到txt文件中,将会保存两个文本文件:result.txt和number.txt。result.txt的格式是(帧序号,框序号,框到左边距离,框到顶上距离,框横长,框竖高,-1,-1,-1,-1),nu
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基于Yolov5-DeepSort的物体计数器,可以统计车流或人流量等 (117个子文件)
Dockerfile 2KB
Dockerfile 821B
.dockerignore 4KB
test3.gif 24.17MB
test.gif 1.97MB
.gitattributes 75B
.gitignore 4KB
.gitignore 89B
.gitkeep 0B
tutorial.ipynb 384KB
train.jpg 59KB
LICENSE 34KB
LICENSE 1KB
README.md 11KB
README.md 5KB
README.md 3KB
bug-report.md 2KB
README.md 1KB
feature-request.md 737B
question.md 140B
README.md 65B
test3.mp4 19.66MB
test3.mp4 13.03MB
test.mp4 4.09MB
test2.mp4 2.7MB
yolov5s.pt 14.11MB
datasets.py 44KB
train.py 33KB
general.py 28KB
count.py 19KB
plots.py 18KB
test.py 17KB
common.py 16KB
wandb_utils.py 16KB
track.py 13KB
yolo.py 13KB
torch_utils.py 12KB
json_logger.py 11KB
loss.py 9KB
detect.py 9KB
metrics.py 9KB
linear_assignment.py 8KB
kalman_filter.py 8KB
autoanchor.py 7KB
train.py 6KB
export.py 6KB
nn_matching.py 5KB
tracker.py 5KB
hubconf.py 5KB
experimental.py 5KB
google_utils.py 5KB
track.py 5KB
io.py 4KB
deep_sort.py 4KB
activations.py 4KB
evaluation.py 3KB
original_model.py 3KB
model.py 3KB
iou_matching.py 3KB
test.py 2KB
preprocessing.py 2KB
feature_extractor.py 2KB
detection.py 1KB
draw.py 1KB
resume.py 1KB
restapi.py 1KB
parser.py 1KB
log_dataset.py 800B
tools.py 734B
__init__.py 500B
log.py 463B
asserts.py 316B
example_request.py 299B
evaluate.py 294B
__init__.py 0B
__init__.py 0B
__init__.py 0B
__init__.py 0B
__init__.py 0B
__init__.py 0B
__init__.py 0B
userdata.sh 1KB
mime.sh 780B
results.txt 266KB
number.txt 24KB
coco_classes.txt 1KB
requirements.txt 599B
requirements.txt 414B
additional_requirements.txt 105B
anchors.yaml 3KB
yolov5-p7.yaml 2KB
yolov5x6.yaml 2KB
yolov5s6.yaml 2KB
yolov5m6.yaml 2KB
yolov5l6.yaml 2KB
yolov5-p6.yaml 2KB
yolov5-p2.yaml 2KB
yolov3-spp.yaml 1KB
yolov3.yaml 1KB
yolov5-panet.yaml 1KB
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