Data

Tools

Indexes

Applications

Experiments

Open Source Science

rdf-yolo

Remote sensing small object detection based on YOLOv11 with RepViT and Dynamic Fusion Module

https://github.com/assiikk/rdf-yolo

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (10.2%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Remote sensing small object detection based on YOLOv11 with RepViT and Dynamic Fusion Module

Basic Info
  • Host: GitHub
  • Owner: AssiiKk
  • License: agpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 2.48 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

RDF-YOLO: Remote Sensing Small Object Detection Based on YOLOv11

RDF-YOLO is an improved object detection model tailored for small object detection in aerial imagery. Built upon the YOLOv11 framework, it integrates a lightweight RepViT module and a dynamic fusion module (DFM) to enhance global feature modeling and multi-scale feature interaction.

🔍 Key Features

  • RepViT Module: Lightweight transformer-based encoder for robust semantic modeling under noisy or low-resolution conditions.
  • DFM (Dynamic Fusion Module): Channel-adaptive, spatially-aware feature fusion design for effective multi-scale context integration.
  • SOTA Performance on AI-TOD: Achieves high accuracy on very small objects while maintaining real-time inference capability.
  • Fully compatible with Ultralytics YOLOv11.

🗂 Project Structure

. ├── datasets/ # Dataset config files (e.g. AITOD.yaml) ├── yolo11m_CF.yaml # Custom model configuration file ├── train.py # Training entry script └── ultralytics/nn/conv.py # Modified YOLOv11 backbone containing RepViT and DFM

This repository is a fork of YOLOv11 with custom modules added to support RDF-YOLO.

⚙️ Environment

  • Python 3.8+
  • PyTorch 2.4.1
  • CUDA 12.4
  • torch-pruning 1.5.2
  • torchvision 0.19.1

Install dependencies with:

bash pip install -r requirements.txt

🚀 Getting Started

Training

python train.py --model yolo11m_CF.yaml --data datasets/AITOD.yaml --epochs 600 --batch 16

📜 License

This project inherits the original YOLOv11 AGPL-3.0 License.

RDF-YOLO was developed for research on lightweight and high-precision small object detection in aerial remote sensing. Feel free to open issues or submit pull requests for improvement!

Owner

  • Login: AssiiKk
  • Kind: user

Citation (CITATION.cff) 

# This CITATION.cff file was generated with https://bit.ly/cffinit

cff-version: 1.2.0
title: Ultralytics YOLO
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Glenn
    family-names: Jocher
    affiliation: Ultralytics
    orcid: 'https://orcid.org/0000-0001-5950-6979'
  - family-names: Qiu
    given-names: Jing
    affiliation: Ultralytics
    orcid: 'https://orcid.org/0000-0003-3783-7069'
  - given-names: Ayush
    family-names: Chaurasia
    affiliation: Ultralytics
    orcid: 'https://orcid.org/0000-0002-7603-6750'
repository-code: 'https://github.com/ultralytics/ultralytics'
url: 'https://ultralytics.com'
license: AGPL-3.0
version: 8.0.0
date-released: '2023-01-10'

GitHub Events

Total
  • Issues event: 1
  • Watch event: 1
  • Delete event: 1
  • Push event: 2
  • Create event: 3
Last Year
  • Issues event: 1
  • Watch event: 1
  • Delete event: 1
  • Push event: 2
  • Create event: 3

Dependencies

examples/YOLO-Series-ONNXRuntime-Rust/Cargo.toml cargo
examples/YOLOv8-ONNXRuntime-Rust/Cargo.toml cargo
docker/Dockerfile docker
  • pytorch/pytorch 2.5.1-cuda12.4-cudnn9-runtime build
examples/YOLOv8-Action-Recognition/requirements.txt pypi
  • transformers *
  • ultralytics *
pyproject.toml pypi
  • matplotlib >=3.3.0
  • numpy >=1.23.0,<=2.1.1
  • opencv-python >=4.6.0
  • pandas >=1.1.4
  • pillow >=7.1.2
  • psutil *
  • py-cpuinfo *
  • pyyaml >=5.3.1
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • torch >=1.8.0
  • torch >=1.8.0,!=2.4.0; sys_platform == 'win32'
  • torchvision >=0.9.0
  • tqdm >=4.64.0
  • ultralytics-thop >=2.0.0
requirements.txt pypi
  • PyYAML *
  • fvcore *
  • matplotlib *
  • numpy *
  • opencv-python *
  • scipy *
  • timm ==0.5.4
  • torch *
  • torch-pruning ==1.5.2
  • torchinfo ==1.8.0
  • tqdm *