ODA is a test-time-augmentation (TTA) tool for 2d object detectors.
For use in Kaggle object detection competitions.
⭐ if it helps you! ;)
ODAch supports YOLOv5, YOLOv8, YOLOv9, YOLOv10, and YOLOv11 models from Ultralytics!
import odach as oda
from ultralytics import YOLO
# Load your YOLO model
model = YOLO('yolov8n.pt') # or yolov5, yolov9, yolov10, yolov11
# Wrap the YOLO model for ODAch
yolo_wrapper = oda.wrap_yolo(model, imsize=640, score_threshold=0.25)
# Define TTA transformations
tta = [oda.HorizontalFlip(), oda.VerticalFlip(), oda.Rotate90Left(), oda.Rotate90Right()]
# Create TTA wrapper
tta_model = oda.TTAWrapper(yolo_wrapper, tta)
# Run inference with TTA
results = tta_model(images)- Multi-version support: YOLOv5, YOLOv8, YOLOv9, YOLOv10, YOLOv11
- Automatic format conversion: Handles YOLO output format automatically
- Batch processing: Process multiple images efficiently
- Configurable thresholds: Adjust confidence and IoU thresholds
- Seamless integration: Works with existing ODAch TTA pipeline
# Advanced YOLO TTA with multiple scales
tta = [
oda.HorizontalFlip(),
oda.VerticalFlip(),
oda.Rotate90Left(),
oda.Rotate90Right(),
oda.Multiply(0.9),
oda.Multiply(1.1)
]
# Multi-scale TTA
scale = [0.8, 0.9, 1.0, 1.1, 1.2]
# Create TTA wrapper with scales
tta_model = oda.TTAWrapper(yolo_wrapper, tta, scale)
# Run inference
results = tta_model(images)See example_yolo_usage.py and YOLO_INTEGRATION_README.md for detailed examples.
ODAch supports both VOC and COCO box formats:
| Format | Description | Coordinates |
|---|---|---|
| VOC (Pascal) | Default format | [x1, y1, x2, y2] |
| COCO | Alternative format | [x, y, width, height] |
import odach as oda
# VOC format (default): [x1, y1, x2, y2]
tta_model = oda.TTAWrapper(model, tta, box_format="voc")
# COCO format: [x, y, width, height]
tta_model = oda.TTAWrapper(model, tta, box_format="coco")
# Also available for wrap_yolo
yolo_wrapper = oda.wrap_yolo(model, box_format="coco")import odach as oda
# Convert VOC to COCO
coco_boxes = oda.voc_to_coco(voc_boxes)
# Convert COCO to VOC
voc_boxes = oda.coco_to_voc(coco_boxes)pip install odach# Using uv (recommended)
uv sync --extra dev
# Run tests
uv run pytest tests/ -vSee Example.ipynb.
The setup is very simple, similar to ttach.
import odach as oda
# Declare TTA variations
tta = [oda.HorizontalFlip(), oda.VerticalFlip(), oda.Rotate90Left(), oda.Multiply(0.9), oda.Multiply(1.1)]
# load image
img = loadimg(impath)
# wrap model and tta
tta_model = oda.TTAWrapper(model, tta)
# Execute TTA!
boxes, scores, labels = tta_model(img)import odach as oda
# Declare TTA variations
tta = [oda.HorizontalFlip(), oda.VerticalFlip(), oda.Rotate90Left(), oda.Multiply(0.9), oda.Multiply(1.1)]
# Declare scales to tta
scale = [0.8, 0.9, 1, 1.1, 1.2]
# load image
img = loadimg(impath)
# wrap model and tta
tta_model = oda.TTAWrapper(model, tta, scale)
# Execute TTA!
boxes, scores, labels = tta_model(img)-
The boxes are also filtered by nms(wbf default).
-
The image size should be square.
-
Wrap your detection model so that the output is similar to torchvision frcnn format:
[{"boxes": [[x,y,x2,y2], ...], "labels": [0,1,..], "scores": [1.0, 0.8, ..]}] -
Example for EfficientDets https://www.kaggle.com/kyoshioka47/example-of-2d-single-scale-tta-with-odach/
# wrap effdet
oda_effdet = oda.wrap_effdet(effdet)
# Declare TTA variations
tta = [oda.HorizontalFlip(), oda.VerticalFlip(), oda.Rotate90Left()]
# Declare scales to tta
scale = [1]
# wrap model and tta
tta_model = oda.TTAWrapper(oda_effdet, tta, scale)example_yolo_usage.py- Basic YOLO integrationYOLO_INTEGRATION_README.md- Detailed YOLO usage guide
nms, wbf are from https://kaggle.com/zfturbo
tta is based on https://github.com/qubvel/ttach, https://github.com/andrewekhalel/edafa/tree/master/edafa and https://www.kaggle.com/shonenkov/wbf-over-tta-single-model-efficientdet