iamhelitha/EleFind-yolo11-elephant

EleFind — YOLOv11 Elephant Detection Model

A YOLOv11 model fine-tuned for detecting elephants in high-resolution aerial and drone imagery, designed to work with SAHI (Slicing Aided Hyper Inference).

Try it in the live demo: EleFind on HuggingFace Spaces

Training Configuration

Parameter	Value
Base model	YOLOv11 (pretrained)
Task	Object detection (single class: elephant)
Image size	1024 x 1024
Epochs	100 (early stopping, patience=20)
Batch size	16
Learning rate	0.01
AMP	Enabled
Augmentation	Mosaic, random augment, erasing (0.4), flip LR (0.5)

Performance (Test Set — 50 images)

Metric	Value
Precision	53.2 %
Recall	49.1 %
F1-Score	51.0 %
mAP@0.5	84.3 %
True Positives	185
False Positives	163
False Negatives	192

SAHI Configuration

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

Training Results

Training curves — loss and metric progression over 100 epochs:

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

  

Sample validation predictions:

Usage

With SAHI (recommended)

from sahi import AutoDetectionModel
from sahi.predict import get_sliced_prediction
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="iamhelitha/EleFind-yolo11-elephant",
    filename="best.pt",
    repo_type="model",
)

model = AutoDetectionModel.from_pretrained(
    model_type="yolov8",  # SAHI uses 'yolov8' for YOLOv8/v11 models
    model_path=model_path,
    confidence_threshold=0.30,
    device="cpu",
)

result = get_sliced_prediction(
    image="aerial_image.jpg",
    detection_model=model,
    slice_height=1024,
    slice_width=1024,
    overlap_height_ratio=0.30,
    overlap_width_ratio=0.30,
    postprocess_type="NMS",
    postprocess_match_threshold=0.40,
)

print(f"Detected {len(result.object_prediction_list)} elephants")

With Ultralytics directly

from ultralytics import YOLO
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="iamhelitha/EleFind-yolo11-elephant",
    filename="best.pt",
    repo_type="model",
)

model = YOLO(model_path)
results = model.predict("aerial_image.jpg", conf=0.30)

Intended Use

Designed for wildlife conservation research — counting and locating elephants in aerial survey imagery.

Limitations

• May not generalise to terrains or lighting conditions outside the training distribution
• Optimised for overhead/nadir aerial views; side-angle photographs will perform poorly
• May miss small or heavily occluded elephants
• False positives can occur on rocks, shadows, or similarly sized objects

Citation

@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

@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}
}

Author

Helitha Guruge — Undergraduate Research Project

License

MIT