Method for facial emotion recognition compitition of Xunfei and Datawhale .

Related tags

Deep Learning emotion-detection emotion-recognition xunfei facial-emotion-recognition face-emotion-detection compitition
Overview

人脸情绪识别挑战赛-第3名-W03KFgNOc-源代码、模型以及说明文档

  1. 队名：W03KFgNOc
  2. 排名：3
  3. 正确率: 0.75564
  4. 队员：yyMoming,xkwang,RichardoMu。
  5. 比赛链接：人脸情绪识别挑战赛
  6. 文章地址:link

emotion

该项目分别训练八个模型并生成csv文件，并进行融合

构建conda环境

conda create -n emotion python==3.8.0
conda activate emotion
cd {project_path}
pip install -r requirements.txt

训练

打开train.sh，可以看到训练的命令行，依次注释和解注释随后运行train.sh。 因为是训练八个模型，分别是efficientnet_b2b, efficientnet_b3b, cbam_resnet50, resmasking,resmasking_dropout1,resnest269e,swin,hrnet_w64,所以要训练和测试，需要分别进行8次。

  1. 训练efficientnet_b2b
python main_fer2013.py --config ./config/efficientnet_b2b_config.json
  1. 训练efficientnet_b3b
python main_fer2013.py --config ./config/efficientnet_b3b_config.json
  1. 训练cbam_resnet50
python main_fer2013.py --config ./config/cbam_resnet50_config.json
  1. 训练hrnet_w64
python main_fer2013.py --config ./config/hrnet_w64_config.json
  1. 训练resmasking
python main_fer2013.py --config ./config/resmasking_config.json
  1. 训练resmasking_dropout1
python main_fer2013.py --config ./config/resmasking_dropout1_config.json
  1. 训练resnest269e
python main_fer2013.py --config ./config/resnest269e_config.json
  1. 训练swin
python main_fer2013.py --config ./config/swin_config.json

checkpoint保存在{project_path}/checkpoint目录下，可以在log文件夹下查看训练的日志。

预测

具体内容在test.sh文件中。各个模型我们存放在百度云盘 https://pan.baidu.com/s/1mM-APWoLV5P3nvrzmG--Jg 提取码 1gyh

下载后复制到user_data/model_data下面即可运行下面的命令进行预测。

  1. 预测efficientnet_b2b
python gen_results.py --config ./config/efficientnet_b2b_config.json --model_name efficientnet_b2b --checkpoint_path efficientnet_b2b_2021Jul25_17.08
  1. 预测efficientnet_b3b
python gen_results.py --config ./config/efficientnet_b3b_config.json --model_name efficientnet_b3b --checkpoint_path efficientnet_b3b_2021Jul25_20.08
  1. 测试cbam_resnet50
python gen_results.py --config ./config/cbam_resnet50_config.json --model_name cbam_resnet50 --checkpoint_path cbam_resnet50_test_2021Jul24_19.18
  1. 测试hrnet_w64
python gen_results.py --config ./config/hrnet_w64_config.json --model_name hrnet_w64 --checkpoint_path hrnet_test_2021Aug01_17.13
  1. 测试resmasking
python gen_results.py --config ./config/resmasking_config.json --model_name resmasking --checkpoint_path resmasking_test_2021Jul26_14.33
  1. 测试resmasking_dropout1
python gen_results.py --config ./config/resmasking_dropout1_config.json --model_name resmasking_dropout1 --checkpoint_path resmasking_dropout1_test_2021Aug01_17.13
  1. 测试resnest269e
python gen_results.py --config ./config/resnest269e_config.json --model_name resnest269e --checkpoint_path resnest269e_test_2021Aug02_11.39
  1. 测试swin
python gen_results.py --config ./config/swin_config.json --model_name swin_large_patch4_window7_224 --checkpoint_path swin_large_patch4_window7_224_test_2021Aug02_21.36

请注意，这里的model_name是确定的，checkpoint_path是你训练得到模型的名字，如果你自己训练了其中的一些模型，请将对应的名称修改为训练得到模型的名称。

集成

上述8个模型的预测结果统一放在user_data/tmp_data里面，下面使用集成方法对上述八个模型的结果进行整合。

python gen_ensemble.py

我们将上述八个模型的结果进行集成，最终生成的文件放在prediction_result下面的result.csv文件中。

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