MODEL_CARD.md

metadata

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

EleFind - YOLOv11 Elephant Detection Model

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

Model Description

This model detects elephants from aerial photographs taken by drones. It was trained on sliced aerial images (1024x1024 patches) and is optimised for use with SAHI to handle full-resolution drone imagery (typically 5472x3648).

Training Configuration

Parameter	Value
Base model	YOLOv11 (pretrained)
Task	Object detection (single class: elephant)
Image size	1024x1024
Epochs	100 (with early stopping, patience=20)
Batch size	16
Optimiser	Auto
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	53.16%
Recall	49.07%
F1-Score	51.03%
True Positives	185
False Positives	163
False Negatives	192

Optimised SAHI Parameters

Parameter	Value
Slice size	1024x1024
Overlap ratio	0.30
Confidence threshold	0.30
IoU threshold (NMS)	0.40

Usage

With SAHI (recommended for high-res images)

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

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

# Load with SAHI
model = AutoDetectionModel.from_pretrained(
    model_type="yolov8",  # SAHI uses 'yolov8' for YOLOv8/v11 models
    model_path=model_path,
    confidence_threshold=0.30,
    device="cpu",  # or "cuda:0"
)

# Run sliced prediction
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

This model is designed for wildlife conservation research, specifically for counting and locating elephants in aerial survey imagery. It works best with high-resolution drone photographs.

Limitations

• Trained on a specific dataset of aerial elephant imagery; may not generalise well to different terrains or camera angles
• Optimised for overhead/nadir aerial views; side-angle photographs will perform poorly
• Small elephants or heavily occluded elephants may be missed
• False positives can occur on rocks, shadows, or other objects of similar size/shape

Author

Helitha Guruge — Undergraduate Research Project

License

MIT