<div align="center">
<p>
<a align="left" href="https://ultralytics.com/yolov5" target="_blank">
<img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg"></a>
</p>
<br>
<div>
<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>
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
<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>
<br>
<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>
<a href="https://join.slack.com/t/ultralytics/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg"><img src="https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack" alt="Join Forum"></a>
</div>
<br>
<p>
YOLOv5 馃殌 is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a>
open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
</p>
<div align="center">
<a href="https://github.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://www.linkedin.com/company/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://twitter.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://www.producthunt.com/@glenn_jocher">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-producthunt.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://youtube.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://www.facebook.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://www.instagram.com/ultralytics/">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="2%"/>
</a>
</div>
<!--
<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
<img width="800" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png"></a>
-->
</div>
## <div align="center">Documentation</div>
See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.
## <div align="center">Quick Start Examples</div>
<details open>
<summary>Install</summary>
Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a
[**Python>=3.7.0**](https://www.python.org/) environment, including
[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
```bash
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install
```
</details>
<details open>
<summary>Inference</summary>
Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)
. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest
YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
```python
import torch
# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5l, yolov5x, 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.
```
</details>
<details>
<summary>Inference with detect.py</summary>
`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/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
img.jpg # image
vid.mp4 # video
path/ # directory
path/*.jpg # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
```
</details>
<details>
<summary>Training</summary>
The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)
results. [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). Training times for YOLOv5n/s/m/l/x are
1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the
largest `--batch-size` possible, or pass `--batch-size -1` for
YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.
```bash
python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128
yolov5s 64
yolov5m 40
yolov5l 24
yolov5x 16
```
<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
</details>
<details open>
<summary>Tutorials</summary>
- [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)聽 馃殌 RECOMMENDED
- [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)聽 鈽橈笍
RECOMMENDED
- [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)聽 馃専 NEW
- [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975)聽 馃専 NEW
- [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)聽 猸� NEW
- [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) 馃殌
- [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)聽 猸� NEW
- [Architecture Summary](https://github.com/ultralytics/yolov5/issues/6998)聽 猸� NEW
</details>
## <div align="center">Environments</div>
Get started in seconds with our verified environments. Click each icon below for details.
<div align="center">
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
<img src="https://github.com/ultralytics/yolov5/releas
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events.out.tfevents.1651128165.xwqf.20566.0 837KB
libDepthUtils.a 14KB
AUTHORS 324B
node.cc.bak 28KB
node_main.cc.bak 3KB
amcl.launch.xml.bak 3KB
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testmap.bmp 374B
pf.c 18KB
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packet.c 4KB
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usb_reset.c 2KB
map.c 2KB
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node.cc 28KB
map_builder_bridge.cc 21KB
rosbag_validate_main.cc 16KB
offline_node.cc 15KB
msg_conversion.cc 14KB
assets_writer.cc 11KB
submaps_display.cc 10KB
sensor_bridge.cc 9KB
msg_conversion_test.cc 9KB
drawable_submap.cc 8KB
trajectory_options.cc 7KB
occupancy_grid_node_main.cc 7KB
start_trajectory_main.cc 6KB
ogre_slice.cc 6KB
rosbag_publisher_main.cc 6KB
trajectory_comparison_main.cc 5KB
playable_bag.cc 4KB
pbstream_map_publisher_main.cc 4KB
node_main.cc 4KB
ros_map_writing_points_processor.cc 4KB
pbstream_to_ros_map_main.cc 4KB
node_grpc_main.cc 3KB
assets_writer_main.cc 3KB
node_options.cc 2KB
ros_log_sink.cc 2KB
tf_bridge.cc 2KB
submap.cc 2KB
urdf_reader.cc 2KB
ros_map.cc 2KB
time_conversion.cc 2KB
offline_node_grpc_main.cc 2KB
time_conversion_test.cc 1KB
offline_node_main.cc 1KB
configuration_files_test.cc 1KB
node_constants.cc 1KB
split_string.cc 1KB
TebLocalPlannerReconfigure.cfg 17KB
UVCCamera.cfg 7KB
AMCL.cfg 6KB
BaseLocalPlanner.cfg 4KB
Astra.cfg 3KB
MoveBase.cfg 3KB
DWAPlanner.cfg 3KB
GlobalPlanner.cfg 2KB
setup.cfg 2KB
Costmap2D.cfg 2KB
ObstaclePlugin.cfg 1KB
VoxelPlugin.cfg 1KB
params.cfg 735B
InflationPlugin.cfg 616B
LocalPlannerLimits.cfg 309B
GenericPlugin.cfg 276B
FindSUITESPARSE.cmake 5KB
FindG2O.cmake 3KB
FindEigen3.cmake 3KB
config 279B
config 267B
teb_local_planner_ros.cpp 53KB
optimal_planner.cpp 52KB
amcl_node.cpp 50KB
move_base.cpp 49KB
nvilidar_driver_serialport.cpp 48KB
nvilidar_driver_udp.cpp 46KB
astra_driver.cpp 40KB
trajectory_planner.cpp 35KB
module_tests.cpp 35KB
homotopy_class_planner.cpp 28KB
navfn.cpp 28KB
slam_gmapping.cpp 28KB
trajectory_planner_ros.cpp 25KB
teb_config.cpp 23KB
astra_device.cpp 23KB
obstacle_layer.cpp 22KB
point_grid.cpp 22KB
timed_elastic_band.cpp 21KB
camera_driver.cpp 20KB
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