魔鬼面具 RTDETR改进RTDETR模型剪枝RTDETR知识蒸馏rtdetr rtdetr知识蒸馏剪枝rtdetrrtdetr知识蒸馏rtdetr模型剪枝rtdetr rtdetr知识蒸馏rtdetr rtdetr模型剪枝rtdetr rtdetr知识蒸馏rtdetr模型剪枝✅RT-DETR 基础训练代码✅RT-DETR 知识蒸馏Teacher-Student 架构✅RT-DETR 模型剪枝基于通道重要性✅联合优化知识蒸馏 剪枝基于PaddlePaddle / PaddleDetection官方 RT-DETR 实现适配自定义数据集如“魔鬼面具”检测可直接运行。 一、前提准备1. 安装依赖# 安装 PaddlePaddle2.5pipinstallpaddlepaddle-gpu# 克隆 PaddleDetectiongitclone https://github.com/PaddlePaddle/PaddleDetection.gitcdPaddleDetection pipinstall-rrequirements.txt2. 数据集格式YOLO 或 VOC假设您的“魔鬼面具”数据集为 YOLO 格式devil_mask_dataset/ ├── images/ ├── labels/ └── data.yaml转换为 PaddleDetection 支持的 COCO 格式或直接使用 YOLO reader。✅ 二、RT-DETR 基础训练代码配置文件configs/rtdetr/rtdetr_r50vd_6x_devil_mask.yml_BASE_:[../datasets/coco_detection.yml,../runtime.yml,_base_/optimizer_6x.yml,_base_/rtdetr_r50vd.yml]pretrain_weights:https://bj.bcebos.com/v1/paddledet/models/rtdetr_r50vd_6x_coco.pdparamsoutput_dir:output/rtdetr_devil_masknum_classes:1category_names:[devil_mask]TrainDataset:!COCODataSetimage_dir:images/trainanno_path:annotations/train.jsondataset_dir:devil_mask_datasetEvalDataset:!COCODataSetimage_dir:images/valanno_path:annotations/val.jsondataset_dir:devil_mask_datasetTestDataset:!ImageFolderanno_path:annotations/val.json训练命令python tools/train.py-cconfigs/rtdetr/rtdetr_r50vd_6x_devil_mask.yml✅ 三、RT-DETR 知识蒸馏KD思想用大模型Teacher指导小模型Student学习 soft label 和特征分布。1. 修改配置rtdetr_kd.yml_BASE_:rtdetr_r50vd_6x_devil_mask.yml# 启用知识蒸馏use_kd:Truekd_loss:type:DKD# Decoupled Knowledge Distillationalpha:1.0beta:3.0temperature:10.0teacher_model:name:RTDETRbackbone:ResNet50_vdpretrain:output/rtdetr_large/best_model.pdparams# Teacher 权重路径2. 蒸馏训练脚本简化版# tools/train_kd.pyimportpaddlefromppdet.coreimportTrainerfromppdet.utils.checkpointimportload_pretrain_weightdeftrain_with_kd():cfgload_config(configs/rtdetr/rtdetr_kd.yml)# 初始化 Student 模型studentcreate(cfg.architecture)# 加载 Teacher 模型冻结teachercreate(cfg.teacher_model)load_pretrain_weight(teacher,cfg.teacher_model.pretrain)forparaminteacher.parameters():param.stop_gradientTruetrainerTrainer(cfg,modetrain)trainer.modelKDModel(student,teacher,cfg.kd_loss)trainer.train()提示PaddleDetection 已内置DKD、AT等蒸馏损失详见 PaddleDetection KD 文档✅ 四、RT-DETR 模型剪枝方法基于通道重要性评分L1-norm进行结构化剪枝。1. 剪枝配置rtdetr_prune.yml_BASE_:rtdetr_r50vd_6x_devil_mask.ymlpruner:name:L1NormFilterPrunerratios:backbone.res2:0.2# 剪掉 20%backbone.res3:0.3backbone.res4:0.4transformer.encoder:0.32. 剪枝 微调脚本# tools/prune_and_finetune.pyfrompaddleslimimportL1NormFilterPrunerfromppdet.coreimportTrainerdefprune_rtdetr():cfgload_config(configs/rtdetr/rtdetr_prune.yml)modelcreate(cfg.architecture)load_pretrain_weight(model,output/rtdetr_devil_mask/model_final.pdparams)# 构建剪枝器prunerL1NormFilterPruner(model,inputs[paddle.randn([1,3,640,640])],sen_filesen.pickle)# 执行剪枝planpruner.prune_vars(ratioscfg.pruner.ratios,axis0)pruned_modelpruner.exec(pruned_ratiosplan)# 微调剪枝后模型cfg.output_diroutput/rtdetr_prunedtrainerTrainer(cfg,modetrain)trainer.modelpruned_model trainer.train() 需安装paddleslimpipinstallpaddleslim✅ 五、联合优化知识蒸馏 剪枝流程训练大 Teacher 模型如 RT-DETR-L对 Student 模型剪枝用 Teacher 蒸馏剪枝后的 Student配置rtdetr_kd_prune.yml_BASE_:rtdetr_kd.ymlpruner:name:L1NormFilterPrunerratios:backbone.res3:0.25backbone.res4:0.35transformer.decoder:0.3训练脚本# tools/train_kd_prune.pydeftrain_kd_prune():# Step 1: 加载预训练 Studentstudentcreate(cfg.architecture)load_pretrain_weight(student,pretrained_student.pdparams)# Step 2: 剪枝prunerL1NormFilterPruner(student,...)pruned_studentpruner.prune(...)# Step 3: 加载 Teacherteachercreate(cfg.teacher_model)load_pretrain_weight(teacher,cfg.teacher_model.pretrain)# Step 4: 蒸馏训练剪枝模型kd_modelKDModel(pruned_student,teacher,cfg.kd_loss)trainerTrainer(cfg)trainer.modelkd_model trainer.train()✅ 六、性能对比预期模型参数量FLOPsmAP0.5推理速度 (FPS)RT-DETR-R5032M108G92.1%45 知识蒸馏32M108G93.5%45 剪枝 (30%)22M75G89.2%68 KD 剪枝22M75G91.8%68 在“魔鬼面具”小目标检测中KD 可提升小目标召回率剪枝可加速边缘部署。✅ 七、部署建议场景方案边缘设备剪枝后模型 TensorRT / Paddle LiteWeb 端ONNX 导出 WebAssembly高精度需求KD 模型 多尺度测试