YOLOv5-Lite：lighter, faster and easier to deploy

Perform a series of ablation experiments on yolov5 to make it lighter (smaller Flops, lower memory, and fewer parameters) and faster (add shuffle channel, yolov5 head for channel reduce. It can infer at least 10+ FPS On the Raspberry Pi 4B when input the frame with 320×320) and is easier to deploy (removing the Focus layer and four slice operations, reducing the model quantization accuracy to an acceptable range).

Comparison of ablation experiment results

ID	Model	Input_size	Flops	Params	Size（M）	[email protected]	[email protected]:0.95
001	yolo-fastest	320×320	0.25G	0.35M	1.4	24.4	-
002	nanodet-m	320×320	0.72G	0.95M	1.8	-	20.6
003	yolo-fastest-xl	320×320	0.72G	0.92M	3.5	34.3	-
004	YOLOv5-Litesours	320×320	0.97G	1.54M	3.3	36.1	20.9
005	yolov3-tiny	416×416	6.96G	6.06M	23.0	33.1	16.6
006	yolov4-tiny	416×416	5.62G	8.86M	33.7	40.2	21.7
007	YOLOv5-Litesours	416×416	1.63G	1.54M	3.3	41.3	24.3
008	YOLOv5-Litecours	640×640	8.6G	4.37M	9.2	52.5	33.0
009	YOLOv5s	640×640	17.0G	7.3M	14.2	55.8	35.9
010	YOLOv5-Litegours	640×640	15.7G	5.3M	10.9	56.9	38.1

Comparison on different platforms

Equipment	Computing backend	System	Input	Framework	v5Lite-s	v5Lite-c	v5Lite-g	YOLOv5s
Inter	@i5-10210U	window(x86)	640×640	openvino	-	46ms	-	131ms
Nvidia	@RTX 2080Ti	Linux(x86)	640×640	torch	-	-	15ms	14ms
Redmi K30	@Snapdragon 730G	Android(arm64)	320×320	ncnn	28ms	-	-	163ms
Raspberrypi 4B	@ARM Cortex-A72	Linux(arm64)	320×320	ncnn	84ms	-	-	371ms
Raspberrypi 4B	@ARM Cortex-A72	Linux(arm64)	320×320	mnn	76ms	-	-	356ms

• The above is a 4-thread test benchmark
• Raspberrypi 4B enable bf16s optimization，Raspberrypi 64 Bit OS

qq交流群：993965802

·Model Zoo·

@YOLOv5-Lites:

Model	Size	Backbone	Head	Framework	Design for
v5Lite-s.pt	3.3m	shufflenetv2（Megvii）	v5Lites-head	Pytorch	Arm-cpu
v5Lite-s.bin v5Lite-s.param	3.3m	shufflenetv2	v5Lites-head	ncnn	Arm-cpu
v5Lite-s-int8.bin v5Lite-s-int8.param	1.7m	shufflenetv2	v5Lites-head	ncnn	Arm-cpu
v5Lite-s.mnn	3.3m	shufflenetv2	v5Lites-head	mnn	Arm-cpu
v5Lite-s-int4.mnn	987k	shufflenetv2	v5Lites-head	mnn	Arm-cpu
v5Lite-s-fp16.bin v5Lite-s-fp16.xml	3.4m	shufflenetv2	v5Lites-head	openvivo	x86-cpu
v5Lite-s-fp32.bin v5Lite-s-fp32.xml	6.8m	shufflenetv2	v5Lites-head	openvivo	x86-cpu
v5Lite-s-fp16.tflite	3.3m	shufflenetv2	v5Lites-head	tflite	arm-cpu
v5Lite-s-fp32.tflite	6.7m	shufflenetv2	v5Lites-head	tflite	arm-cpu
v5Lite-s-int8.tflite	1.8m	shufflenetv2	v5Lites-head	tflite	arm-cpu

@YOLOv5-Litec:

Model	Size	Backbone	Head	Framework	Design for
v5Lite-c.pt	9m	PPLcnet（Baidu）	v5Litec-head	Pytorch	x86-cpu / x86-vpu
v5Lite-c.bin v5Lite-c.xml	8.7m	PPLcnet	v5Litec-head	openvivo	x86-cpu / x86-vpu

@YOLOv5-Liteg:

Model	Size	Backbone	Head	Framework	Design for
v5Lite-g.pt	10.9m	Repvgg（Tsinghua）	v5Liteg-head	Pytorch	x86-gpu / arm-gpu / arm-npu
v5Lite-g-int8.engine	8.5m	Repvgg	v5Liteg-head	Tensorrt	x86-gpu / arm-gpu / arm-npu
v5lite-g-int8.tmfile	8.7m	Repvgg	v5Liteg-head	Tengine	arm-npu

Download Link：

• YOLOv5—Lites.pt: | Baidu Drive | Google Drive |
  

|──────ncnn-fp16: | Baidu Drive | Google Drive |
|──────ncnn-int8: | Baidu Drive | Google Drive |
|──────mnn-fp16: | Baidu Drive | Google Drive |
|──────mnn-int4: | Baidu Drive | Google Drive |
└──────tengine-fp32: | Baidu Drive | Google Drive |

• YOLOv5—Litec.pt: Baidu Drive | Google Drive |
  

└──────openvino-fp16: | Baidu Drive | Google Drive |

• YOLOv5—Liteg.pt: | Baidu Drive | Google Drive |
  

Baidu Drive Password: pogg

v5lite-s model: TFLite Float32, Float16, INT8, Dynamic range quantization, ONNX, TFJS, TensorRT, OpenVINO IR FP32/FP16, Myriad Inference Engin Blob, CoreML

https://github.com/PINTO0309/PINTO_model_zoo/tree/main/180_YOLOv5-Lite

Thanks for PINTO0309:https://github.com/PINTO0309/PINTO_model_zoo/tree/main/180_YOLOv5-Lite

How to use

Install

Python>=3.6.0 is required with all requirements.txt installed including PyTorch>=1.7:

$ git clone https://github.com/ppogg/YOLOv5-Lite
$ cd YOLOv5-Lite
$ pip install -r requirements.txt

Inference with detect.py

detect.py runs inference on a variety of sources, downloading models automatically from the latest YOLOv5-Lite release and saving results to runs/detect.

$ python detect.py --source 0  # webcam
                            file.jpg  # image 
                            file.mp4  # video
                            path/  # directory
                            path/*.jpg  # glob
                            'https://youtu.be/NUsoVlDFqZg'  # YouTube
                            'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

Training

$ python train.py --data coco.yaml --cfg v5lite-s.yaml --weights v5lite-s.pt --batch-size 128
                                         v5lite-c.yaml           v5lite-c.pt               96
                                         v5lite-g.yaml           v5lite-g.pt               64

If you use multi-gpu. It's faster several times:

$ python -m torch.distributed.launch --nproc_per_node 2 train.py

DataSet

Training set and test set distribution （the path with xx.jpg）

train: ../coco/images/train2017/
val: ../coco/images/val2017/

├── images            # xx.jpg example
│   ├── train2017        
│   │   ├── 000001.jpg
│   │   ├── 000002.jpg
│   │   └── 000003.jpg
│   └── val2017         
│       ├── 100001.jpg
│       ├── 100002.jpg
│       └── 100003.jpg
└── labels             # xx.txt example      
    ├── train2017       
    │   ├── 000001.txt
    │   ├── 000002.txt
    │   └── 000003.txt
    └── val2017         
        ├── 100001.txt
        ├── 100002.txt
        └── 100003.txt

model hub

Here, the original components of YOLOv5 and the reproduced components of YOLOv5-Lite are organized and stored in the model hub：

Updating ...

How to deploy

ncnn for arm-cpu

mnn for arm-cpu

openvino x86-cpu or x86-vpu

tensorrt for arm-gpu or arm-npu or x86-gpu

Android for arm-cpu

Android_demo

This is a Redmi phone, the processor is Snapdragon 730G, and yolov5-lite is used for detection. The performance is as follows:

link: https://github.com/ppogg/YOLOv5-Lite/tree/master/ncnn_Android

Android_v5Lite-s: https://drive.google.com/file/d/1CtohY68N2B9XYuqFLiTp-Nd2kuFWgAUR/view?usp=sharing

Android_v5Lite-g: https://drive.google.com/file/d/1FnvkWxxP_aZwhi000xjIuhJ_OhqOUJcj/view?usp=sharing

More detailed explanation

Detailed model link:

[1] https://zhuanlan.zhihu.com/p/400545131

[2] https://zhuanlan.zhihu.com/p/410874403

[3] https://blog.csdn.net/weixin_45829462/article/details/119787840

[4] https://zhuanlan.zhihu.com/p/420737659

Reference

https://github.com/ultralytics/yolov5

https://github.com/megvii-model/ShuffleNet-Series

https://github.com/Tencent/ncnn