DER (Dynamic Enhancement for Object Detection)

This repository contains trained model weights for the DER (Dynamic Enhancement for object detection) project.

Repository Structure

Model/
├── baseline/              # Baseline model weights
│   ├── VisDrone2019/
│   ├── UAVDT/
│   ├── TinyPerson/
│   └── DOTAv1/
└── DER_improved/          # DER-enhanced model weights
    ├── VisDrone2019/
    ├── UAVDT/
    ├── TinyPerson/
    └── DOTAv1/

Supported Models

• RTMDet-R2: Real-time object detector with rotated bounding boxes
  • Scales: tiny, small
  • Format: .pth (PyTorch)
• PP-PicoDet: Lightweight object detection model from PaddlePaddle
  • Scales: m (medium), l (large)
  • Format: .pdparams (PaddlePaddle)
• YOLOv11: (Coming soon)
• RT-DETR: (Coming soon)

Datasets

• VisDrone2019: Drone-based object detection dataset
• UAVDT: UAV-based detection and tracking dataset
• TinyPerson: Small object detection dataset
• DOTAv1: Dataset for Object deTection in Aerial images

File Naming Convention

Files are named following the pattern: ModelName-Scale-Dataset.ext

Examples:

• PP-PicoDet-l-VisDrone2019.pdparams
• RTMDet-R2-tiny-UAVDT.pth

Available Weights

PP-PicoDet

• baseline: 8 weight files (2 scales × 4 datasets)
• DER_improved: 8 weight files (2 scales × 4 datasets)

RTMDet-R2

• baseline: 8 weight files (2 scales × 4 datasets)
• DER_improved: 8 weight files (2 scales × 4 datasets)

Usage

from huggingface_hub import hf_hub_download

# Download PP-PicoDet baseline weights
weight_path = hf_hub_download(
    repo_id="Nahuyiur/DER",
    filename="PP-PicoDet/baseline/VisDrone2019/PP-PicoDet-l-VisDrone2019.pdparams"
)

# Download RTMDet-R2 DER-improved weights
weight_path = hf_hub_download(
    repo_id="Nahuyiur/DER",
    filename="RTMDet-R2/DER_improved/TinyPerson/RTMDet-R2-small-TinyPerson.pth"
)

License

Please refer to the original model repositories for license information.