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README.md

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+---
+license: apache-2.0
+tags:
+  - object-detection
+  - wildlife
+  - thermal
+  - rgb
+  - drone
+  - ultralytics
+  - yolo
+datasets:
+  - custom
+pipeline_tag: object-detection
+library_name: ultralytics
+---
+
+# 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](https://github.com/ultralytics/ultralytics) framework on 1024×1024 px drone images.
+
+---
+
+## Models Overview
+
+| Model | Modality | Dataset | Epochs | mAP50 | mAP50-95 | Notes |
+|---|---|---|---|---|---|---|
+| `thermal_original` | Thermal | Original thermal dataset | 25 | 0.217 | — | Baseline thermal model |
+| `thermal_merged` | Thermal | Original + supplemental thermal data | 23 | 0.390 | 0.250 | Refined thermal model |
+| `rgb` | RGB | Full RGB dataset | 72 | 0.946 | 0.655 | Primary RGB model |
+| `matched_rgb` | RGB | Matched RGB-thermal pairs | 43 | 0.731 | 0.431 | Cross-modal comparison |
+| `matched_thermal` | Thermal | Matched RGB-thermal pairs | 27 | 0.719 | 0.289 | Cross-modal comparison |
+
+> **Matched models** were trained on the same spatially co-registered scene pairs to enable fair modality comparison.
+
+---
+
+## Training Details
+
+All models share the following configuration:
+
+| Parameter | Value |
+|---|---|
+| Base model | `yolo26x.pt` |
+| Image size | 1024 × 1024 px |
+| Batch size | 4 |
+| Max epochs | 200 |
+| Early stopping patience | 20 epochs |
+| Optimizer | Auto |
+| AMP (mixed precision) | Enabled |
+| Close mosaic | Last 10 epochs |
+| Data augmentation | RandAugment, erasing (p=0.4), fliplr (p=0.5) |
+
+---
+
+## 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/
+    └── ...
+```
+
+---
+
+## Quick Start
+
+### Installation
+
+```bash
+pip install ultralytics
+```
+
+### Load a model and run inference
+
+```python
+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
+
+```python
+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)
+
+```python
+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.
+
+---
+
+## Results Visualizations
+
+Training curves, precision-recall curves, and validation batch predictions are included in each model subdirectory (`.png` / `.jpg` files).
+
+---
+
+## License
+
+Apache 2.0 — see `LICENSE`.