flexible-yolov5

Update the code for ultralytics/yolov5 version 6.1.

代码基于U版YOLOv5 6.1版本. 根据 {backbone, neck, head} 重新组织了网络结构, 目前backbone 除了原始的YOLO外，还可选择 resnet, hrnet, swin-transformer, gnn, mobilenet 等主流backbone. 同时也可以自由的加入 SE, DCN, drop block 等插件. 可以很方便的对网络结构等进行替换、修改、实验. 同时提供了tensorrt 的c++、Python 推理, 量化. 以及Triton、tf_serving 部署代码. 每个backbone只选了一个训练300个epoch做对比，均无预训练权重，由于网络结构不同，我的结果并不能代表网络最终的结果，可以作为一个baseline参考. 这个项目适合想要各种改YOLO或者验证模块. 是如果你有什么好的idea，比如增加新的backbone, 插件等, 欢迎提PR, 使用时遇到什么问题, 也欢迎提issue. 如果对你有帮助, 感谢给颗♥(ˆ◡ˆԅ)小 ⭐️⭐️.

Split the yolov5 model to {backbone, neck, head} to facilitate the operation of various modules and support more backbones.Basically, only change the model, and I didn't change the architecture, training and testing of yolov5. Therefore, if the original code is updated, it is also very convenient to update this code. if you have some new ideas, you can give a pull request, add new features together。 if this repo can help you, please give me a star.

Table of contents

• Features
• Notices
• Bugs
• Prerequisites
• Getting Started
  • Dataset Preparation
  • Training and Testing
    • Training
    • Testing and Visualize
  • Model performance comparison
  • Detection
  • Deploy
    • Export
    • Grpc server
• Reference

Features

• Reorganize model structure, such as backbone, neck, head, can modify the network flexibly and conveniently
• mobilenetV3-small, mobilenetV3-large
• shufflenet_v2_x0_5, shufflenet_v2_x1_0, shufflenet_v2_x1_5, shufflenet_v2_x2_0
• yolov5s, yolov5m, yolov5l, yolov5x, yolov5transformer
• resnet18, resnet50, resnet34, resnet101, resnet152
• efficientnet_b0 - efficientnet_b8, efficientnet_l2
• hrnet 18,32,48
• CBAM, SE
• swin transformer - base, tiny, small, large (please set half=False in scripts/eval.py and don't use model.half in train.py)
• DCN (mixed precision training not support, if you want use dcn, please close amp in line 292 of scripts/train.py)
• coord conv
• drop_block
• vgg, repvgg
• tensorrt c++/python infer, triton server infer
• gnn backbone

Notices

• The CBAM, SE, DCN, coord conv. At present, the above plug-ins are not added to all networks, so you may need to modify the code yourself.
• The default gw and gd for PAN and FPN of other backbone are same as yolov5_s, so if you want a strong model, please modify self.gw and self.gd in FPN and PAN.
• resnet with dcn, training on gpu *RuntimeError: expected scalar type Half but found Float: please remove the mixed precision training in line 351 of scripts/train.py
• swin-transformer, training is ok, but testing report *RuntimeError: expected object of scalar type Float but got scalar type Half for argument #2 'mat2' in call to_th_bmm_out in swin_trsansformer.py. please set half=False in script/eval.py
• mobilenet export onnx failed, please replace HardSigmoid() by others, because onnx don't support pytorch nn.threshold

Bugs

None

Prerequisites

please refer requirements.txt

Getting Started

Dataset Preparation

Make data for yolov5 format. you can use od/data/transform_voc.py convert VOC data to yolov5 data format.

Training and Testing

For training and Testing, it's same like yolov5.

Training

1. check out configs/data.yaml, and replace with your data， and number of object nc
2. check out configs/model_*.yaml, choose backbone. and change nc to your dataset. please refer support_backbone in models.backbone.init.py

$ python scripts/train.py  --batch 16 --epochs 5 --data configs/data.yaml --cfg configs/model_XXX.yaml

A google colab demo in train_demo.ipynb

Testing and Visualize

$ python scripts/eval.py   --data configs/data.yaml  --weights runs/train/yolo/weights/best.py

Model performance comparison with different backbone

For some reasons, I can't provide the pretrained weight, only the comparison results. Sorry!

All checkpoints are trained to 300 epochs with default settings, all backbones without pretrained weights. Yolov5 Nano and Small models use hyp.scratch-low.yaml hyps, all others use hyp.scratch-high.yaml. The mAP of the validation come to the last epoch, maybe not the best.

flexible-yolov5 model with different backbones	size (pixels)	mAPval 0.5:0.95	mAPval 0.5	params
[flexible-YOLOv5n](https://pan.baidu.com/s/1UAvEmgWmpxA3oPm5CJ8C-g 提取码: kg22)	640	25.7	43.3	1872157
[flexible-YOLOv5s](https://pan.baidu.com/s/1ImN2ryMK3IPy8_St-Rzxhw 提取码: pt8i)	640	35	54.7	7235389
[flexible-YOLOv5m]	640	42.1	62	21190557
[flexible-YOLOv5l]	640	45.3	65.3	46563709
[flexible-YOLOv5x]	640	47	66.7	86749405
others backbone
[mobilnet-v3-small]	640	21.9	37.6	3185757
[resnet-18]	640	34.6	53.7	14240445
[shufflenetv2-x1_0]	640	27.8	45.1	4297569
[repvgg-A0]	640
[vgg-16bn]	640	35.2	56.4	17868989
[efficientnet-b1]	640	38.1	58.6	9725597
[swin-tiny]	640	39.2	60.5	30691127
[gcn-tiny]	640	33.8	55.5	131474444
resnet with plug-in
[resnet-18-cbam]	640	35.2	55.5	15620399
[resnet-18-dcn]	640

Detection

python scripts/detector.py   --weights yolov5.pth --imgs_root  test_imgs   --save_dir  ./results --img_size  640  --conf_thresh 0.4  --iou_thresh 0.4

Deploy

Export

python scripts/export.py   --weights yolov5.pth 

Grpc Server

In projects folder, tf_serving and triton demo are provided.

Quantization

You can directly quantify the onnx model

This script run succ on Tensorrt 7.x. For 8.x, this code need be rewrite.

python scripts/trt_quant/convert_trt_quant.py  --img_dir  /XXXX/train/  --img_size 640 --batch_size 6 --batch 200 --onnx_model runs/train/exp1/weights/bast.onnx  --mode int8

See

Tensorrt Inference

For tensorrt model, you can direct use official trt export, and refer scripts/trt_infer/cpp/. For test, I use TensorRT-8.4.0.6.

privode c++ / python demo, scripts/trt_infer

Reference

• ultralytics/yolov5
• EfficientNet-PyTorch
• Mobilenet v3
• resnet
• hrnet
• shufflenet
• swin transformer
• dcn-v2
• coord_conv
• triton server
• drop_block
• trt quan
• repvgg