odach 0.2.0

ODAch is a test-time-augmentation tool for pytorch 2d object detectors with YOLO support.

ODAch, An Object Detection TTA tool for Pytorch

ODA is a test-time-augmentation (TTA) tool for 2d object detectors.

For use in Kaggle object detection competitions.

:star: if it helps you! ;)

🚀 YOLO Integration (New!)

ODAch now supports YOLOv5, YOLOv8, and newer YOLO models from Ultralytics! This is the most important feature for modern object detection workflows.

Quick Start with YOLO

import odach as oda
from ultralytics import YOLO

# Load your YOLO model
model = YOLO('yolov8n.pt')  # or yolov5, yolov6, yolov7, yolov8, yolov9

# 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)

YOLO Features

• Multi-version support: YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv9
• 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

YOLO TTA Example

# 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.

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Install

pip install odach

Usage

See Example.ipynb.

The setup is very simple, similar to ttach.

Singlescale TTA

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)

Multiscale TTA

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.

Model Output Wrapping

• Wrap your detection model so that the output is similar to torchvision frcnn format: [["box":[[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)

Examples

YOLO TTA Examples

• example_yolo_usage.py - Basic YOLO integration
• YOLO_INTEGRATION_README.md - Detailed YOLO usage guide

Global Wheat Detection

Example notebook

Thanks

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