YOLOAir : Make the improvement of the YOLO model faster, more convenient

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The YOLOAir algorithm library is a combination toolbox of a series of YOLO detection algorithms based on PyTorch. Used to combine different modules to build different networks.

✨Built-in model network structures such as YOLOv5, YOLOv7, YOLOX, YOLOR, Transformer, Scaled_YOLOv4, YOLOv3, YOLOv4, YOLO-Facev2, TPH-YOLOv5, YOLOv5Lite, PicoDet (continuously updated🚀)...

✨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, easy module combination, and more powerful network model building.


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✨Based on the YOLOv5 code framework, and synchronously adapt to the stable YOLOv5_v6.1 update, synchronize the v6.1 deployment ecosystem. Before using this project, you can learn about the YOLOv5 library.

Features🚀 • Usage🍉 • Documentation📒 • issues🌟 • News💪

support

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

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, YOLOR model network structure, YOLOX model network structure, Scaled_YOLOv4 model network structure, YOLOv4 model network structure, YOLOv3 model network structure, YOLO-FaceV2 model network structure, TPH-YOLOv5 model network structure The structure, YOLOv5-Lite model network structure, PicoDet model network structure, etc. are continuously updated...

The above multiple detection algorithms use a unified model code framework, which is integrated in the YOLOAir library, with a unified task form and a unified application method. 🌟It is convenient for researchers to improve the algorithm model of the paper, compare models, and realize the diversification of network combinations. including lightweight models and models with higher accuracy, which can be reasonably selected according to the scene, and achieve a balance between accuracy and speed. 🌟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.

🔥🔥🔥 Heavy! ! ! As a supplement to the open source project of the attention mechanism, a 6300+ 🌟Star attention mechanism algorithm code base is strongly recommended 👉External-Attention-pytorch，The summary is very comprehensive, including various Attention, Self-Attention and other codes. The code is concise and easy to read, and one line of code implements the Attention mechanism. Welcome everyone to play!

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

🚀 Support more Backbone

CSPDarkNet series
ResNet series
RepBlock series
ShuffleNet series
Ghost series
MobileNet series
ConvNext series
RepLKNet series
RegNet series
EfficientNet series
Transformer series
CNN and Transformer, etc.
Continuously updated 🎈

🚀 Support more Necks

The neck includes mainstream structures such as FPN, PANet, and BiFPN. Continuously updated 🎈

🚀Support more detection heads

YOLOv4、YOLOv5 Head、
YOLOR Implicit Head、
YOLOX Decoupled Head、DetectX Head
Adaptively Spatial Feature Fusion Head(ASFF)、
YOLOv7 IAuxDetect Head, IDetect Head；

🚀Support for more plug-and-play attention mechanisms

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
DANet Attention and other mainstream attention mechanisms

🚀Support for more IoU loss functions

Loss functions such as CIoU, DIoU, GIoU, EIoU, SIoU, alpha IOU, etc.

🚀 More spatial pyramid pooling structures

SPP / SPPF / ASPP / RFB / SPPCSPC, etc.

🚀Support more NMS

NMS、Merge-NMS、DIoU-NMS、Soft-NMS、CIoU_NMS、DIoU_NMS、GIoU_NMS、EIoU_NMS、SIoU_NMS、Soft-SIoUNMS、Soft-CIoUNMS、Soft-DIoUNMS、Soft-EIoUNMS、Soft-GIoUNMS, etc.

🚀Support for more data augmentation

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

🚀Support more Loss

ComputeLoss、ComputeNWDLoss、ComputeLoss(X)、ComputeLoss(v6)、ComputeLossAuxOTA(v7)、ComputeLossOTA(v7), etc.

🚀Supports Anchor-base and Anchor-Free

🚀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 etc. Detailed code search in ./models/common.py file🔍👉Corresponding module link
👉Network Model Structure Diagram

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.

Built-in network model configuration support ✨

🚀Including yaml configuration of YOLOv3, YOLOv4, Scaled_YOLOv4, YOLOv5, YOLOv7, YOLOX, YOLOR, Transformer, YOLO-FaceV2, PicoDet, YOLOv5-Lite, TPH-YOLOv5 ** other algorithm models such as improved network structure** File Summary (Recommended 🌟🌟🌟🌟🌟)

News!

Support SPD-Conv✅
Support HorNet Network ✅
Support ConvNext module ✅
🔥Add the difference of YOLOv3, YOLOv4, YOLOv5, YOLOv7, Scaled-YOLOv4 network pre-training weight link 🔗
Updating...

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)}
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 -

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The original version was created based on YOLOv5

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

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The original version was created based on YOLOv5-6.0

YOLO network model specific improvement method tutorial and principle reference

More module improvement explanation tutorials are continuously updated...

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. Follow up: YOLO-mask & YOLO-pose

Statement

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Acknowledgements

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README_EN.md

README_EN.md

YOLOAir : Make the improvement of the YOLO model faster, more convenient

support

Main features🚀

Built-in network model configuration support ✨

News!

Usage🍉

Install

Training

Inference

fusion

Performance

YOLO network model specific improvement method tutorial and principle reference

YOLOv5 official tutorial ✨

Future enhancement ✨

Statement

Acknowledgements

Files

README_EN.md

Latest commit

History

README_EN.md

File metadata and controls

YOLOAir : Make the improvement of the YOLO model faster, more convenient

support

Main features🚀

Built-in network model configuration support ✨

News!

Usage🍉

Install

Training

Inference

fusion

Performance

YOLO network model specific improvement method tutorial and principle reference

YOLOv5 official tutorial ✨

Future enhancement ✨

Statement

Acknowledgements