WhaleDrone yolo26n-obb Model
This repo contains yolo26n-obb-best.pt, the best checkpoint from a YOLO26n oriented bounding-box (OBB) model fine-tuned on the WhaleDrone Los Cabos humpback whale UAV dataset.
Model
- Task: Oriented bounding-box object detection
- Base model:
yolo26n-obb.pt - Checkpoint:
yolo26n-obb-best.pt - Classes:
whale,boat,dolphin - Parameters: YOLO26n model variant
The filtered dataset contains 450 images and 997 labeled instances. It has 407 training images / 914 instances and 43 validation images / 83 instances. The dataset contains 858 whale instances, 139 boat instances, and no dolphin instances.
Training
| Dataset | Link |
|---|---|
| Original | WhaleDrone |
| ML-Ready | WhaleDrone ML-Ready |
Training was performed with Ultralytics using the following main settings:
- 100 epochs
- Image size:
1024 - Batch size:
16 - Optimizer:
auto - Pretrained initialization enabled
- Automatic mixed precision enabled
- Seed:
0 - Deterministic training enabled
The dataset configuration is available at
dataset.yaml, with the
complete dataset summary in
yolo_dataset_filtered/README.md.
Validation performance
The best epoch was selected by validation mAP50-95 from the training run:
| Epoch | Precision | Recall | mAP50 | mAP50-95 |
|---|---|---|---|---|
| 72 | 0.9469 | 0.9053 | 0.9472 | 0.7772 |
These metrics are aggregate validation results reported by Ultralytics for the OBB task. They should be interpreted with the class imbalance and the absence of dolphin examples in mind.
Inference
Install Ultralytics, then run inference with:
yolo obb predict \
model=best.pt \
source=/path/to/image-or-video \
imgsz=1024
For Python:
from ultralytics import YOLO
model = YOLO("best.pt")
results = model.predict(source="/path/to/image-or-video", imgsz=1024)
The model predicts oriented boxes for whales and boats in this filtered dataset. Because no dolphin instances were present during training or validation, dolphin predictions are not expected to be reliable.