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
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:
1yolo obb predict \
2 model=best.pt \
3 source=/path/to/image-or-video \
4 imgsz=1024
For Python:
1from ultralytics import YOLO
2
3model = YOLO("best.pt")
4results = 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.
