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Nahuyiur/
DER

paddlepaddle

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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

python

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.