# 技巧

* https://github.com/rwightman/pytorch-image-models
* [本科生晋升GM记录 & kaggle比赛进阶技巧分享](https://zhuanlan.zhihu.com/p/93806755)
* [9 Computer Vision Tricks to Improve Performance](https://www.kaggle.com/competitions/happy-whale-and-dolphin/discussion/310105)
* https://github.com/kuangliu/pytorch-cifar

## 数据

* https://zhuanlan.zhihu.com/p/402511359
* 数据增量: https://github.com/conradry/copy-paste-aug
* 目标检测增强: 正样本增强：根据类别增强、根据大小增强、增强中加入随机概率、无合适anchor的增强、根据匹配好坏的增强

## 训练

* 首先在小backbone如resnet34/efficientnetb0起步，调节loss/augmentation/metric/lr\_scheduler
* 然后再换大模型

## 分类

## 检测

mmdetection: https://zhuanlan.zhihu.com/p/337375549

* EDA：分析图片尺寸信息，标签数量、大小信息(根据数据信息决定：anchor，训练尺度， 感受野) https://github.com/Media-Smart/volkscv/tree/master/volkscv/analyzer/statistics
* 模型：yolov5，efficientdet，mmdetection(cascade rcnn)
* 调参：模型，后处理
* 优化：persudo label，TTA，nms，weighted fusion box
* https://github.com/felixBrave/ocr\_chinese
* https://github.com/FudanVI/benchmarking-chinese-text-recognition

### EDA

* 图像大小分布
* gt-box 大小，长宽比分布
* gt-box与图像大小比例分布
* 特殊性质

## 后处理

* https://www.kaggle.com/shonenkov/wbf-approach-for-ensemble

## 入门

* [深度迁移学习实用技巧(Kaggle Paris Day 2022)](https://www.bilibili.com/video/BV1xP411f7B1/?spm_id_from=333.999.0.0\&vd_source=2ba9453e6f2ab94ba2912e691fb70b02)
* [Google-Deep Learning Tuning Playbook](https://github.com/google-research/tuning_playbook)
* https://www.kaggle.com/code/phalanx/train-swin-t-pytorch-lightning

https://zhuanlan.zhihu.com/p/266864780\
https://www.zybuluo.com/huanghaian/note/1743266\
https://github.com/LiChenyang-Github/tianchi\_Cervical\_Cancer\_top4\
https://www.kaggle.com/code/youhanlee/unet-resnetblock-hypercolumn-deep-supervision-fold

## 实践

* https://zhuanlan.zhihu.com/p/409157142
* https://zhuanlan.zhihu.com/p/363354912
* https://kulbear.github.io/archives/protein/
* https://github.com/yl305237731/flexible-yolov5
* https://github.com/bubbliiiing/yolov5-tf2

MM: https://zhuanlan.zhihu.com/p/337375549?utm\_source=zhihu\&utm\_medium=social\&utm\_oi=896443966524891136

https://towardsdatascience.com/indian-car-license-plate-detection-using-yolo-v5-ae2574578175

* https://github.com/facebookresearch/pytorchvideo
