README.md

metadata

title: EleFind - Aerial Elephant Detection
emoji: 🐘
colorFrom: green
colorTo: blue
sdk: gradio
sdk_version: 6.8.0
app_file: app.py
python_version: '3.10'
suggested_hardware: cpu-basic
license: mit
tags:
  - object-detection
  - yolo
  - yolov11
  - sahi
  - computer-vision
  - elephant-detection
  - wildlife-conservation
  - aerial-imagery
pinned: false

EleFind — Aerial Elephant Detection

A web application for detecting elephants in aerial and drone imagery using YOLOv11 with SAHI (Slicing Aided Hyper Inference) and explainable AI heatmap visualizations.

Features

• Real-time elephant detection with bounding boxes and confidence scores
• XAI Gaussian density heatmaps highlighting detection hotspots
• Adjustable SAHI parameters (confidence, slice size, overlap, IoU)
• Confidence bar charts and per-detection data tables
• Automatic model download from HuggingFace Hub

Model

Property	Value
Architecture	YOLOv11 (Ultralytics)
Training data	Sliced aerial elephant imagery (1024 x 1024 patches)
Inference	SAHI with NMS post-processing
Precision	53.2 %
Recall	49.1 %
F1-Score	51.0 %
mAP@0.5	84.3 %

SAHI Configuration

Parameter	Value
Slice size	1024 x 1024
Overlap ratio	0.30
Confidence threshold	0.30
IoU threshold	0.40

Training Results

Training curves — loss convergence and metric progression over 100 epochs:

Normalized confusion matrix and Precision-Recall curve (mAP@0.5 = 0.843):

  

Sample validation predictions — detections on held-out aerial tiles:

Getting Started

git clone https://github.com/iamhelitha/EleFind-gradio-ui.git
cd EleFind-gradio-ui
pip install -r requirements.txt

# Run the app (model auto-downloads from HuggingFace)
python app.py

# Run tests
pytest test_detection.py -v
pytest test_detection.py -v -m "not slow"   # skip inference tests

Environment Variables

Variable	Description	Default
HF_MODEL_REPO	HuggingFace model repository	iamhelitha/EleFind-yolo11-elephant
HF_MODEL_FILE	Model filename in the repository	best.pt

Project Structure

EleFind-gradio-ui/
├── app.py               # Gradio web application (HF Spaces entry point)
├── test_detection.py    # Pytest test suite
├── requirements.txt     # Python dependencies
├── packages.txt         # System-level dependencies (HF Spaces)
├── pytest.ini           # Pytest configuration
├── MODEL_CARD.md        # Model card
├── examples/            # Sample aerial images for the demo
└── assets/              # Training visualizations for documentation

Tech Stack

• Ultralytics YOLOv11 — object detection
• SAHI — slicing aided hyper inference for high-resolution images
• Gradio — web UI framework
• HuggingFace Hub — model hosting and Spaces deployment

Citation

If you use EleFind in your work, please cite:

@software{guruge2025elefind,
  title     = {EleFind: Aerial Elephant Detection using YOLOv11 and SAHI},
  author    = {Guruge, Helitha},
  year      = {2025},
  url       = {https://github.com/iamhelitha/EleFind-gradio-ui}
}

Acknowledgments

This project is built on the following works:

@dataset{naude2019aerial,
  title     = {The Aerial Elephant Dataset},
  author    = {Naud\'{e}, Johannes J. and Joubert, Deon},
  year      = {2019},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.3234780},
  url       = {https://zenodo.org/records/3234780}
}

@software{jocher2023ultralytics,
  title     = {Ultralytics YOLO},
  author    = {Jocher, Glenn and Qiu, Jing and Chaurasia, Ayush},
  year      = {2023},
  version   = {8.0.0},
  url       = {https://github.com/ultralytics/ultralytics},
  license   = {AGPL-3.0}
}

@article{akyon2022sahi,
  title     = {Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection},
  author    = {Akyon, Fatih Cagatay and Altinuc, Sinan Onur and Temizel, Alptekin},
  journal   = {2022 IEEE International Conference on Image Processing (ICIP)},
  doi       = {10.1109/ICIP46576.2022.9897990},
  pages     = {966--970},
  year      = {2022}
}

@article{abid2019gradio,
  title     = {Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild},
  author    = {Abid, Abubakar and Abdalla, Ali and Abid, Ali and Khan, Dawood and Alfozan, Abdulrahman and Zou, James},
  journal   = {arXiv preprint arXiv:1906.02569},
  year      = {2019}
}

Author

Helitha Guruge — Undergraduate Research Project

License

MIT