bambi-models

Wildlife Detection with YOLOv26 — Drone RGB & Thermal Models

A collection of five YOLOv26x models fine-tuned for wildlife detection in drone imagery, supporting both RGB and thermal (infrared) modalities. Models were trained using the Ultralytics framework on 1024×1024 px drone images.

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Models Overview

Matched models were trained on the same spatially co-registered scene pairs to enable fair modality comparison.

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Training Details

All models share the following configuration:

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Repository Structure

.
├── README.md
├── inference.py                  # Sample inference code
├── thermal_original/
│   ├── weights/
│   │   ├── best.pt               # Best checkpoint
│   │   └── last.pt               # Last checkpoint
│   ├── args.yaml                 # Training configuration
│   ├── results.csv               # Per-epoch training metrics
│   └── results.png               # Training curves
├── thermal_merged/
│   └── ...
├── rgb/
│   └── ...
├── matched_rgb/
│   └── ...
└── matched_thermal/
    └── ...

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Quick Start

Installation

pip install ultralytics

Load a model and run inference

from ultralytics import YOLO

# Choose your model
model = YOLO("rgb/weights/best.pt")          # RGB drone imagery
# model = YOLO("thermal_merged/weights/best.pt")  # Thermal imagery

# Run inference on an image
results = model("path/to/your/image.jpg")

# Display / save results
results[0].show()
results[0].save("output.jpg")

# Access detections
for box in results[0].boxes:
    print(f"Class: {box.cls.item()}, Conf: {box.conf.item():.2f}, BBox: {box.xyxy[0].tolist()}")

Batch inference

from ultralytics import YOLO
from pathlib import Path

model = YOLO("rgb/weights/best.pt")

# Run on a folder of images
results = model(
    source="path/to/images/",
    imgsz=1024,
    conf=0.25,
    iou=0.45,
    save=True,
    project="detections",
    name="run"
)

Modality comparison (matched dataset)

from ultralytics import YOLO

rgb_model     = YOLO("matched_rgb/weights/best.pt")
thermal_model = YOLO("matched_thermal/weights/best.pt")

# Run both models on co-registered image pairs
rgb_results     = rgb_model("rgb_frame.jpg",     imgsz=1024, conf=0.25)
thermal_results = thermal_model("thermal_frame.png", imgsz=1024, conf=0.25)

print(f"RGB detections:     {len(rgb_results[0].boxes)}")
print(f"Thermal detections: {len(thermal_results[0].boxes)}")

See inference.py for a complete script with CLI argument parsing.

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Results Visualizations

Training curves, precision-recall curves, and validation batch predictions are included in each model subdirectory (.png / .jpg files).

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License

Apache 2.0 — see LICENSE.