---
library_name: ultralytics
tags:
- object-detection
- yolo
- yolov11
- sahi
- elephant-detection
- wildlife-conservation
- aerial-imagery
- computer-vision
license: mit
pipeline_tag: object-detection
model-index:
- name: EleFind YOLOv11 Elephant Detector
  results:
  - task:
      type: object-detection
    metrics:
    - type: precision
      value: 53.16
    - type: recall
      value: 49.07
    - type: f1
      value: 51.03
    - type: map
      value: 0.843
      name: mAP@0.5
language:
- en
base_model:
- Ultralytics/YOLO11
---

# 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](https://github.com/obss/sahi) (Slicing Aided Hyper Inference).

Try it in the live demo: [EleFind on HuggingFace Spaces](https://huggingface.co/spaces/iamhelitha/EleFind-gradio-ui)

## 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:

<p align="center">
  <img src="https://raw.githubusercontent.com/iamhelitha/EleFind-gradio-ui/main/assets/results.png" alt="Training curves" width="100%"/>
</p>

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

<p align="center">
  <img src="https://raw.githubusercontent.com/iamhelitha/EleFind-gradio-ui/main/assets/confusion_matrix_normalized.png" alt="Confusion matrix" width="45%"/>
  &nbsp;&nbsp;
  <img src="https://raw.githubusercontent.com/iamhelitha/EleFind-gradio-ui/main/assets/BoxPR_curve.png" alt="Precision-Recall curve" width="45%"/>
</p>

**Sample validation predictions:**

<p align="center">
  <img src="https://raw.githubusercontent.com/iamhelitha/EleFind-gradio-ui/main/assets/val_batch0_pred.jpg" alt="Validation predictions" width="80%"/>
</p>

## Usage

### With SAHI (recommended)

```python
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

```python
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

```bibtex
@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

```bibtex
@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