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https://github.com/Bobo-y/flexible-yolov5
More readable and flexible yolov5 with more backbone(gcn, resnet, shufflenet, moblienet, efficientnet, hrnet, swin-transformer, etc) and (cbam，dcn and so on), and tensorrt
https://github.com/Bobo-y/flexible-yolov5
backbone cbam dcnv2 gcn hrnet moblienet neck object-detection ptq pytorch qat resnet shufflenet sparsity swin-transformer tensorrt triton-server yolov3 yolov5
Last synced: 4 days ago
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More readable and flexible yolov5 with more backbone(gcn, resnet, shufflenet, moblienet, efficientnet, hrnet, swin-transformer, etc) and (cbam，dcn and so on), and tensorrt
- Host: GitHub
- URL: https://github.com/Bobo-y/flexible-yolov5
- Owner: Bobo-y
- License: gpl-3.0
- Created: 2021-03-26T11:09:37.000Z (over 3 years ago)
- Default Branch: v2
- Last Pushed: 2024-08-19T02:36:03.000Z (3 months ago)
- Last Synced: 2024-08-19T03:42:31.856Z (3 months ago)
- Topics: backbone, cbam, dcnv2, gcn, hrnet, moblienet, neck, object-detection, ptq, pytorch, qat, resnet, shufflenet, sparsity, swin-transformer, tensorrt, triton-server, yolov3, yolov5
- Language: Python
- Homepage:
- Size: 12.4 MB
- Stars: 661
- Watchers: 10
- Forks: 120
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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- awesome-yolo-object-detection - Bobo-y/flexible-yolov5 - y/flexible-yolov5?style=social"/> : More readable and flexible yolov5 with more backbone(resnet, shufflenet, moblienet, efficientnet, hrnet, swin-transformer) and (cbam，dcn and so on), and tensorrt. (Lighter and Deployment Frameworks)
- awesome-yolo-object-detection - Bobo-y/flexible-yolov5 - y/flexible-yolov5?style=social"/> : More readable and flexible yolov5 with more backbone(resnet, shufflenet, moblienet, efficientnet, hrnet, swin-transformer) and (cbam，dcn and so on), and tensorrt. (Lighter and Deployment Frameworks)
README
# flexible-yolov5
*Update the code for [ultralytics/yolov5](https://github.com/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](#features)
* [Notices](#Notices)
* [Bugs](#Bugs)
* [Prerequisites](#prerequisites)
* [Getting Started](#getting-started)
* [Dataset Preparation](#dataset-preparation)
* [Training and Testing](#Training-and-Testing)
* [Training](#training)
* [Testing and Visualize](#testing-and-visualize)
* [Model performance comparison](#Model-performance-comparison-with-different-backbone)
* [Detection](#Detection)
* [Deploy](#Deploy)
* [Export](#Export)
* [Grpc server](#Grpc-server)
* [Reference](#Reference)
## Features
- support nvidia tensor-core 4:2 sparsity.
- support QAT, for qat onnx export, need torch >=1.13(I only test on this version) 2023-10-17
- update PTQ code , 2023-10-01
- 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
3.
```shell script
$ python scripts/train.py --batch 16 --epochs 5 --data configs/data.yaml --cfg configs/model_XXX.yaml
```
```shell
# for nvidia tensor-core 4:2 sparsity, install apex
git clone https://github.com/NVIDIA/apex
cd apex
# if pip >= 23.1 (ref: https://pip.pypa.io/en/stable/news/#v23-1) which supports multiple `--config-settings` with the same key...
pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./
# otherwise
pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --global-option="--cpp_ext" --global-option="--cuda_ext" ./
```
A google colab demo in train_demo.ipynb
#### Testing and Visualize
```shell script
$ 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
```shell
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
```shell
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
```shell
python scripts/trt_quant/generate_int8_engine.py --onnx path --images-dir img_path --save-engine engine_path
```
[See](scripts/trt_quant/README)
#### 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](https://github.com/ultralytics/yolov5)
* [EfficientNet-PyTorch](https://github.com/lukemelas/EfficientNet-PyTorch)
* [Mobilenet v3](https://arxiv.org/abs/1905.02244)
* [resnet](https://arxiv.org/abs/1512.03385)
* [hrnet](https://arxiv.org/abs/1908.07919)
* [shufflenet](https://arxiv.org/abs/1707.01083)
* [swin transformer](https://github.com/SwinTransformer/Swin-Transformer-Object-Detection)
* [dcn-v2](https://github.com/jinfagang/DCNv2_latest)
* [coord_conv](https://github.com/mkocabas/CoordConv-pytorch)
* [triton server](https://github.com/triton-inference-server/server)
* [drop_block](https://github.com/miguelvr/dropblock)
* [trt quan](https://github.com/Wulingtian/nanodet_tensorrt_int8_tools)
* [repvgg](https://github.com/DingXiaoH/RepVGG)
## ↳ Contributors
## ↳ Stargazers
[](https://github.com/Bobo-y/flexible-yolov5/stargazers)
## ↳ Forkers
[](https://github.com/Bobo-y/flexible-yolov5/network/members)