YOLO Air : Makes improvements easy again

YOLOAir algorithm library is a combination toolbox of a series of YOLO detection algorithms based on PyTorch.🚀

Unified model code framework, unified application, unified improvement, easy module combination, building a more powerful network model.

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特性🚀 • 使用🍉 • 文档📒 • 报告问题🌟 • 更新💪 • 讨论✌️

Introduction

Model diversification: Build different detection network models based on different network modules.

Module componentization: Help users customize and quickly combine Backbone, Neck, Head, diversify network models, help scientific research improve detection algorithms, model improvements, and network permutation and combination 🏆. Build powerful network models.

Unified model code framework, unified application method, unified parameter adjustment, unified improvement, Integrated multitasking, easy module combination, and more powerful network model building.

Built-in YOLOv5, YOLOv7, YOLOv6, YOLOX, YOLOR, Transformer, PP-YOLO, PP-YOLOv2, PP-YOLOE, PP-YOLOEPlus, Scaled_YOLOv4, YOLOv3, YOLOv4, YOLO-Facev2, TPH-YOLO, YOLOv5Lite, SPD-YOLO, SlimNeck -Model network structures such as YOLO and PicoDet (continuously updated 🚀)...

The above multiple detection algorithms and related multi-task models use a unified model code framework, which is integrated in the YOLOAir library, and the application method is unified. It is convenient for researchers to improve the algorithm model of the paper, compare the models, and realize the diversification of the network combination. It includes lightweight models and models with higher accuracy, which are reasonably selected according to the scene, and strike a balance between accuracy and speed. At the same time, the library supports decoupling of different structures and module components to make modules componentized. By combining different module components, users can customize and build different detection models according to different data sets or different business scenarios.

Support integrated multi-task, including object detection, instance segmentation, image classification, pose estimation, face detection, object tracking and other tasks


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Star🌟、Fork

Project address 🌟: https://github.com/iscyy/yoloair

Based on the YOLOv5 code framework, and synchronously adapted and updated.

🌟 Beta Supported Updates

The following functions are supported in YOLOAir-Beta version 🔥

Complete update to support 20+ Transformer series and their variant backbone networks🌟, multiple MLP networks🌟 and most re-parameterized structural model networks 🚀🚀🚀
Complete update support Graph Neural Network🌟 application in YOLO
Complete update to support multi-modal 🔥 application in YOLO
Complete update to support 30+ Attention attention mechanisms 🌟,
Complete update support Various Head detection heads
Completed update to support YOLOv6 model-v2.0 paper version
Complete update support YOLO series network model heatmap visualization (GardCAM, GardCAM++, etc.) Support models such as YOLOv3, YOLOv3-SPP, YOLOv4, YOLOv5, YOLOR, YOLOv7, Scaled_YOLOv4, TPH-YOLO, SPD-YOLO, PP-YOLO and custom network models
Support Adaptive Training Sample Selection label assignment strategy and Task Alignment Learning label assignment strategy
Finished updating the integrated PPYOLOE algorithm model
Complete update integration TOOD algorithm
other various

🌟 To Do

Updated to support multitasking, including object detection, classification, instance segmentation tasks

Main Features 🚀

🚀 Support more YOLO series algorithm model improvements (continuous update...)

The YOLOAir algorithm library summarizes a variety of mainstream YOLO series detection models, and a set of codes integrates multiple models:

Built-in integrated YOLOv5 model network structure, YOLOv7 model network structure, YOLOv6 model network structure, PP-YOLO model network structure, PP-YOLOv2 model network structure, PP-YOLOE model network structure, PP-YOLOEPlus model network structure, YOLOR model network structure , YOLOX model network structure, ScaledYOLOv4 model network structure, YOLOv4 model network structure, YOLOv3 model network structure, YOLO-FaceV2 model network structure, TPH-YOLOv5 model network structure, SPD-YOLO model network structure, SlimNeck-YOLO model network structure, YOLOv5 -Lite model network structure, PicoDet model network structure, etc. are continuously updated...

🚀Support loading the official pre-training weights of YOLOv3, YOLOv4, YOLOv5, YOLOv7, YOLOR, Scaled_YOLO and other networks for transfer learning

🚀Support more Backbone

CSPDarkNet
HorNet
ResNet
RegNet
ShuffleNet
Ghost
MobileNet
EfficientNet
ConvNext
RepLKNet
RepVGG
RepMLP
ACNet
RepConv
OREPA
Mobileone
自注意力Transformer
MobileViT
BoTNet-Transfomrer
CoTNet-Transfomrer
Swin-Transfomrer
Swin-Transfomrer(v2)
and other trans series Continuous update 🎈🚀🚀🚀

🚀Support more Neck

FPN
PANet
RepPAN
BiFPN
CSPPAN
SlimNeck
ELANPAN Continuous update 🎈🚀🚀🚀

🚀Support more Head

YOLOv4、YOLOv5 Head;
YOLOR Head;
YOLOX Decoupled Head、DetectX Head;
ASFF Head;
YOLOv6-v2.0 Efficient decoupled head;
YOLOv7 IAuxDetect Head, IDetect Head;
PPYOLOE Efficient Task-aligned head with DFL and VFL
Other different detection heads

🚀Support more plug-and-play attention mechanisms Attention

Plug-and-play use of attention mechanisms anywhere in the network
Self Attention
Contextual Transformer
Bottleneck Transformer
S2-MLP Attention
SK Attention
CBAM Attention
SE Attention
Coordinate attention
NAM Attention
GAM Attention
ECA Attention
Shuffle Attention
CrissCrossAttention
Coordinate attention
SOCAttention
SimAM Attention Continuously updated 🎈

🚀More Spatial Pyramid Pooling Structures

SPP
SPPF
ASPP
RFB
SPPCSPC
SPPFCSPC
SimSPPF Continuously updated 🎈

🚀Support more Loss

ComputeLoss
ComputeLoss(v5)
ComputeLoss(v6)
ComputeLoss(X)
ComputeLossAuxOTA(v7)
ComputeLossOTA(v7)
ComputeNWDLoss
other Loss Continuously updated 🎈

🚀Support Anchor-base 和 Anchor-Free

🌟 YOLOv5、YOLOv7、YOLOv3、YOLOv4、YOLOR、ScaledYOLOv4、PPYOLO、PPYOLOv2、Improved-YOLOv5、Improved-YOLOv7
🌟 YOLOX、YOLOv6 (Paper)、PPYOLOE、PPYOLOE+

🚀Supports multiple label assignment strategies

Multi Anchor
YOLOv5 Label assignment
SimOTA Label assignment
YOLOv7 Label assignment
Adaptive Training Sample Selection Label assignment
Task Alignment Learning Label assignment
Other Improved Label Assignment Strategies Continuously updated 🎈

🚀Supports Weighted Box Fusion (WBF)

🚀 Built-in multiple network model modular components Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, C3HB, C3RFEM, MultiSEAM, SEAM, C3STR, SPPCSPC, RepConv, BoT3, Air, CA, CBAM, Involution, Stem, ResCSPC, ResCSPB, ResXCSPB, ResXCSPC, BottleneckCSPB, BottleneckCSPC, ASPP, BasicRFB, SPPCSPC_group, HorBlock, CNeB,C3GC ,C3C2, nn.ConvTranspose2d, DWConvblock, RepVGGBlock, CoT3, ConvNextBlock, SPPCSP, BottleneckCSP2, DownC, BottleneckCSPF, RepVGGBlock, ReOrg, DWT, MobileOne,HorNet...等详细代码 ./models/common.py文件 内

🚀Support for more IoU loss functions

CIoU
DIoU
GIoU
EIoU
SIoU
alpha IOU
Continuously updated 🎈

🚀Support more NMS

NMS
Merge-NMS
Soft-NMS
CIoU_NMS
DIoU_NMS
GIoU_NMS
EIoU_NMS
SIoU_NMS
Soft-SIoUNMS、Soft-CIoUNMS、Soft-DIoUNMS、Soft-EIoUNMS、Soft-GIoUNMS等;
Continuously updated 🎈

🚀Support for more data enhancements

Mosaic、Copy paste、Random affine(Rotation, Scale, Translation and Shear)、MixUp、Augment HSV(Hue, Saturation, Value、Random horizontal flip

🚀 YOLO series network model heat map visualization (GardCAM, GardCAM++, etc.) Supports models such as YOLOv3, YOLOv3-SPP, YOLOv4, YOLOv5, YOLOR, YOLOv7Scaled_YOLOv4, TPH-YOLO, SPD-YOLO and custom network models (YOLOAir(Beta version)🔥Supported)

🚀Mainstream network model structure diagram summary: Model 🔗

The above component modules use a unified model code framework, unified task form, and unified application method. Module componentization🚀 can help users customize and quickly combine Backbone, Neck, and Head, diversify network models, and help scientific research improve detection algorithms. Build stronger network models.

Note: The above main features support include Main version and Beta version. Some features are only updated in Beta temporarily, not all updates are directly in Main, and will be gradually added to Main version after internal testing of subsequent Beta versions.

Built-in network model configuration support ✨

🚀 Including various improved networks based on YOLOv5, YOLOv7, YOLOX, YOLOR, YOLOv3, YOLOv4, Scaled_YOLOv4, PPYOLO, PPYOLOE, PPYOLOEPlus, Transformer, YOLO-FaceV2, PicoDet, YOLOv5-Lite, TPH-YOLOv5, SPD-YOLO, etc.** Model configuration files for algorithmic models such as structures**

Effect preview🚀

Object detection	instance segmentation

Image classification	instance segmentation

instance segmentation	Object tracking

pose estimation	face detection

Heatmap visualization01	Heatmap visualization02

Pre-trained weights 🚀

YOLOv5 https://github.com/ultralytics/yolov5/releases/tag/v6.1
YOLOv4 https://github.com/iscyy/yoloair/releases/tag/v1.0
YOLOv3 https://github.com/iscyy/yoloair/releases/tag/v1.0
YOLOR https://github.com/iscyy/yoloair/releases/tag/v1.0
Scaled_YOLO https://github.com/iscyy/yoloair/releases/tag/v1.0
YOLOv7 https://github.com/iscyy/yoloair/releases/tag/v1.0

Usage🍉

About the code. Follow the design principle of YOLOv5.
The original version was created based on YOLOv5(v6.1)

Install

Clone repo and install requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7.

$ git clone https://github.com/iscyy/yoloair.git
$ cd yoloair
$ pip install -r requirements.txt

Training

$ python train.py --data coco128.yaml --cfg configs/yolov5/yolov5s.yaml

Inference

detect.py runs inference on a variety of sources, saving results to runs/detect.

$ python detect.py --source 0  # webcam
                          img.jpg  # image
                          vid.mp4  # video
                          path/  # directory
                          path/*.jpg  # glob

fusion

If you use different models to infer on the dataset, you can use the wbf.py file to integrate the results via weighted box fusion. You just need to set img path and txt path in wbf.py file.

$ python wbf.py

Performance

Model	size ^(pixels)	mAP^val 0.5:0.95	mAP^val 0.5	Speed ^{CPU b1 (ms)}	Speed ^{V100 b1 (ms)}	Speed ^{V100 b32 (ms)}	params ^(M)	FLOPs ^{@640 (B)}	Weights
YOLOv5n	640	28.0	45.7	45	6.3	0.6	1.9	4.5	YOLOv5n
YOLOv5s	640	37.4	56.8	98	6.4	0.9	7.2	16.5	YOLOv5s
YOLOv5m	640	45.4	64.1	224	8.2	1.7	21.2	49.0	YOLOv5m
YOLOv5l	640	49.0	67.3	430	10.1	2.7	46.5	109.1	YOLOv5l
YOLOv5x	640	50.7	68.9	766	12.1	4.8	86.7	205.7	YOLOv5x

YOLOv5n6	1280	36.0	54.4	153	8.1	2.1	3.2	4.6	YOLOv5n6
YOLOv5s6	1280	44.8	63.7	385	8.2	3.6	12.6	16.8	YOLOv5s6
YOLOv5m6	1280	51.3	69.3	887	11.1	6.8	35.7	50.0	YOLOv5m6
YOLOv5l6	1280	53.7	71.3	1784	15.8	10.5	76.8	111.4	YOLOv5l6
YOLOv5x6 + TTA	1280 1536	55.0 55.8	72.7 72.7	3136 -	26.2 -	19.4 -	140.7 -	209.8 -	YOLOv5x6

Expand

The original version was created based on YOLOv5(6.1)

Model	size ^(pixels)	mAP^val 0.5:0.95	mAP^val 0.5	deconv kernel size	Speed ^{V100 b1 (ms)}	Speed ^{V100 b32 (ms)}	params ^(M)	FLOPs ^{@640 (B)}
YOLOv5s	640	33.7	52.9	-	5.6	2.2	7.23	16.5
YOLOv5s-deconv-exp1	640	33.4	52.5	2	5.6	2.4	7.55	18.2
YOLOv5s-deconv-exp2	640	34.7	54.2	4	5.8	2.5	8.54	23.2

Expand

The training process depends on 4xV100 GPU

# train
python -m torch.distributed.run --nproc_per_node 4 train.py --device 0,1,2,3 --data data/coco.yaml --hyp data/hyps/hyp.scratch-low.yaml  --cfg path/to/model.yaml --batch 512 --epochs 300 --weights ''
# val
python val.py --verbose --data data/coco.yaml --conf 0.001 --iou 0.65 --batch 1 --weights path/to/model.pt

There is a gap between the mAP of YOLOv5s and the official one, here is just for comparison

Model	size ^(pixels)	mAP^val 0.5:0.95	mAP^val 0.5	params ^(M)	FLOPs ^{@640 (B)}
YOLOv5s	640	37.4	56.6	7.226	16.5
YOLOv5s-deconv	640	37.8	57.1	7.232	16.5

Expand

tested the 4x4 depthwise-separable deconv by setting the groups as input_channel
their params number and FLOPS are nearly the same while the new model's mAP is about 0.4 higher than the origin.

Model	size ^(pixels)	mAP^val 0.5:0.95	mAP^val 0.5	params ^(M)	FLOPs ^{@640 (B)}
YOLOv5s	640	37.2	56.0	7.2	16.5
YOLOv5s-C3GC-backbone	640	37.7	57.3	7.5	16.8

Expand

The original version was created based on YOLOv5-6.0

YOLOv5 official tutorial ✨

Sync with YOLOv5 framework

Train Custom Data 🚀 RECOMMENDED
Tips for Best Training Results ☘️ RECOMMENDED
Multi-GPU Training
PyTorch Hub 🌟 NEW
TFLite, ONNX, CoreML, TensorRT Export 🚀
Test-Time Augmentation (TTA)
Model Ensembling
Model Pruning/Sparsity
Hyperparameter Evolution
Transfer Learning with Frozen Layers
Architecture Summary 🌟 NEW
Weights & Biases Logging
Roboflow for Datasets, Labeling, and Active Learning 🌟 NEW
ClearML Logging 🌟 NEW
Deci Platform 🌟 NEW

Future enhancement ✨

In the future, we will continue to build and improve the YOLOAir ecosystem Improve and integrate more YOLO series models, continue to combine different modules to build more different network models Horizontal expansion and introduction of associative techniques, such as semi-supervised learning, etc.

Citation✨

@article{2022yoloair,
  title={{YOLOAir}: Makes improvements easy again},
  author={iscyy},
  repo={github https://github.com/iscyy/yoloair},
  year={2022}
}

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README_EN.md

README_EN.md

YOLO Air : Makes improvements easy again

Support

Introduction

🌟 Beta Supported Updates

🌟 To Do

Main Features 🚀

Built-in network model configuration support ✨

Effect preview🚀

Pre-trained weights 🚀

Usage🍉

Install

Training

Inference

fusion

Performance

YOLOv5 official tutorial ✨

Future enhancement ✨

Citation✨

Statement

Acknowledgements

Files

README_EN.md

Latest commit

History

README_EN.md

File metadata and controls

YOLO Air : Makes improvements easy again

Support

Introduction

🌟 Beta Supported Updates

🌟 To Do

Main Features 🚀

Built-in network model configuration support ✨

Effect preview🚀

Pre-trained weights 🚀

Usage🍉

Install

Training

Inference

fusion

Performance

YOLOv5 official tutorial ✨

Future enhancement ✨

Citation✨

Statement

Acknowledgements