深度学习项目之RT-DETR训练自己数据集

news2025/6/6 15:41:32

RT-DETR

- 1.模型介绍
- 📌 什么是 RT-DETR ？
- 📖 核心改进点
- 📊 结构示意
- 🎯 RT-DETR 优势
- ⚠️ RT-DETR 缺点
- 📈 应用场景
- 📑 论文 & 官方仓库
- 2.模型框架
- 3.Yaml配置文件
- 4.训练脚本
- 5.训练完成截图
- 6.总结与讨论

1.模型介绍

📌 什么是 RT-DETR ？

RT-DETR（Real-Time Detection Transformer） 是百度和视觉学界联合提出的一种端到端实时目标检测方法，是DETR 系列的加速优化版。

它解决了原始 DETR（2020） 推理慢、收敛慢的问题，让 Transformer-based 检测器能在实时速率下工作，同时保持高精度。

📖 核心改进点

模块	原始DETR	RT-DETR
编码器	Transformer Encoder	精简型 Encoder + 动态特征增强
检测头	查询-匹配式 DETR Head	Group-DETR Head（分组预测更高效）
匹配方式	匈牙利匹配	匈牙利匹配（保留，稳定可靠）
上采样	FPN特征拼接	多层特征解耦动态融合
NMS	不用（端到端预测）	不用，直接输出 Top-N 框
收敛速度	极慢（500轮起步）	极快（50轮左右）

📊 结构示意

🌱 下图是简化结构

输入图像
   │
Backbone（ResNet/ConvNext）
   │
Neck（多层特征）
   │
精简 Transformer Encoder
   │
Group DETR Head（查询式）
   │
匈牙利匹配 (训练时)
   │
直接输出预测结果 (无需NMS)

🎯 RT-DETR 优势

✅ 实时速率，接近 YOLOv8 的 FPS
✅ 端到端，不用NMS、anchor
✅ 精度高，尤其是遮挡、细粒度目标表现优异
✅ 支持多种backbone（ResNet50、ResNet101、ConvNeXt）
✅ 高效、轻量，适合工程部署

⚠️ RT-DETR 缺点

⚠️ 对硬件要求较高（特别是显存）
⚠️ 动态查询 Head 复杂，不如 YOLO 系列直观
⚠️ 小目标场景需微调特征提取策略

📈 应用场景

📦 智能制造缺陷检测
📦 智慧城市交通分析
📦 卫星遥感小目标检测
📦 实时安全监控

📑 论文 & 官方仓库

📖 论文：RT-DETR: Real-Time Detection Transformer
📦 仓库：PaddleDetection-RT-DETR

2.模型框架

在这里插入图片描述
##3.数据集配置

# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

# COCO128 dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
# Documentation: https://docs.ultralytics.com/datasets/detect/coco/
# Example usage: yolo train data=coco128.yaml
# parent
# ├── ultralytics
# └── datasets
#     └── coco128  ← downloads here (7 MB)


# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

train: /root/DATA/backup/images/train #
val: /root/DATA/backup/images/val

# Classes
names:
  0: person
  1: bicycle
  2: car
  3: motorcycle
  4: airplane
  5: bus
  6: train
  7: truck
  8: boat
  9: traffic light
  10: fire hydrant
  11: stop sign
  12: parking meter
  13: bench
  14: bird
  15: cat
  16: dog
  17: horse
  18: sheep
  19: cow
  20: elephant
  21: bear
  22: zebra
  23: giraffe
  24: backpack
  25: umbrella
  26: handbag
  27: tie
  28: suitcase
  29: frisbee
  30: skis
  31: snowboard
  32: sports ball
  33: kite
  34: baseball bat
  35: baseball glove
  36: skateboard
  37: surfboard
  38: tennis racket
  39: bottle
  40: wine glass
  41: cup
  42: fork
  43: knife
  44: spoon
  45: bowl
  46: banana
  47: apple
  48: sandwich
  49: orange
  50: broccoli
  51: carrot
  52: hot dog
  53: pizza
  54: donut
  55: cake
  56: chair
  57: couch
  58: potted plant
  59: bed
  60: dining table
  61: toilet
  62: tv
  63: laptop
  64: mouse
  65: remote
  66: keyboard
  67: cell phone
  68: microwave
  69: oven
  70: toaster
  71: sink
  72: refrigerator
  73: book
  74: clock
  75: vase
  76: scissors
  77: teddy bear
  78: hair drier
  79: toothbrush

以COCO为例

3.Yaml配置文件

# Ultralytics YOLO 🚀, AGPL-3.0 license
# RT-DETR-l object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/rtdetr

# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
  # [depth, width, max_channels]
  l: [1.00, 1.00, 1024]

backbone:
  # [from, repeats, module, args]
  - [-1, 1, HGStem, [32, 48]] # 0-P2/4
  - [-1, 6, HGBlock, [48, 128, 3]] # stage 1
  - [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8
  - [-1, 6, HGBlock, [96, 512, 3]] # stage 2
  - [-1, 1, DWConv, [512, 3, 2, 1, False]] # 4-P3/16
  - [-1, 6, HGBlock, [192, 1024, 5, True, False]] # cm, c2, k, light, shortcut
  - [-1, 6, HGBlock, [192, 1024, 5, True, True]]
  - [-1, 6, HGBlock, [192, 1024, 5, True, True]] # stage 3
  - [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 8-P4/32
  - [-1, 6, HGBlock, [384, 2048, 5, True, False]] # stage 4

head:
  - [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 10 input_proj.2
  - [-1, 1, AIFI, [1024, 8]]
  - [-1, 1, Conv, [256, 1, 1]] # 12, Y5, lateral_convs.0
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [7, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14 input_proj.1
  - [[-2, -1], 1, Concat, [1]]
  - [-1, 3, RepC3, [256]] # 16, fpn_blocks.0
  - [-1, 1, Conv, [256, 1, 1]] # 17, Y4, lateral_convs.1
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 19 input_proj.0
  - [[-2, -1], 1, Concat, [1]] # cat backbone P4
  - [-1, 3, RepC3, [256]] # X3 (21), fpn_blocks.1
  - [-1, 1, Conv, [256, 3, 2]] # 22, downsample_convs.0
  - [[-1, 17], 1, Concat, [1]] # cat Y4
  - [-1, 3, RepC3, [256]] # F4 (24), pan_blocks.0
  - [-1, 1, Conv, [256, 3, 2]] # 25, downsample_convs.1
  - [[-1, 12], 1, Concat, [1]] # cat Y5
  - [-1, 3, RepC3, [256]] # F5 (27), pan_blocks.1
  - [[21, 24, 27], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)

4.训练脚本

# @File: train.py
# @Author: chen_song
# @Time: 2025-06-03 17:55


# 10:43开始的
from ultralytics import RTDETR

# Load a COCO-pretrained RT-DETR-l model
weigth = r"/root/CS/ultralytics-main/ultralytics/cfg/models/rt-detr/rtdetr-l.yaml"
model = RTDETR(weigth)

# Display model information (optional)
model.info()

# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data="/root/CS/ultralytics-main/ultralytics/cfg/datasets/coco.yaml",
                      epochs=100,
                      batch=64,
                      imgsz=640,
                      workers=10,
                      amp=True,
                      project=r'/root/CS/ultralytics-main/runs/train',
                      name='exp',)

# Run inference with the RT-DETR-l model on the 'bus.jpg' image
# results = model("path/to/bus.jpg")