z1069614715/objectdetection_script

一些关于目标检测的脚本和改进思路代码，详细请看readme.md

What it solves

This repository provides a comprehensive collection of scripts, modified architectures, and optimization techniques for object detection models. It specifically targets researchers and students who need to implement model improvements, perform model compression (pruning and distillation), or handle dataset conversions for popular frameworks like YOLO and RT-DETR to accelerate academic publication or deployment.

How it works

The project acts as a modular toolkit where users can combine different architectural improvements via YAML configuration files. It provides:

  • Model Modifications: Pre-modified code for YOLOv5 through YOLOv12 and RT-DETR, allowing users to "build" new model variants by swapping modules.
  • Compression Tools: Implementation of channel pruning and knowledge distillation for various YOLO versions and RT-DETR to reduce model size.
  • Data Utilities: Scripts for dataset processing, offline data augmentation, and format conversion (e.g., VOC to COCO).
  • Analysis Tools: Implementation of Grad-CAM for heat-map visualization and various CV attention mechanisms.

Who it’s for

  • Students and Researchers: Those aiming to publish papers (SCI/graduation theses) who need a stable framework for ablation studies and architectural innovation.
  • ML Engineers: Developers looking to lightweight their models for deployment via pruning and distillation.
  • Beginners: Users who want to implement object detection without deep-diving into complex underlying C++/Python architecture.

Highlights

  • Broad Model Support: Covers YOLOv5, v7, v8, v10, v11, v12, RT-DETR, and DEIM/DFine.
  • Modular Design: Uses a "building block" approach via YAML configs to simplify model modification.
  • Advanced Techniques: Includes support for multi-modal training (image-text, image-image) and DINOv3 backbones.
  • Comprehensive Tooling: Includes dataset conversion scripts, offline augmentation, and visualization tools like Grad-CAM.

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