In the realm of computer vision, the use of Test Time Augmentation (TTA) has emerged as a powerful technique to enhance model predictions. Much like the way an artist adds multiple colors to refine their masterpiece, TTA applies random modifications to test images to generate multiple predictions, which are then averaged for a more robust outcome.
Table of Contents
Quick Start
To kickstart your TTA journey, you’ll need to wrap your model with the appropriate TTA wrapper. This increases the efficacy of your predictions significantly. Here’s how to do it:
- For Segmentation Models:
import ttach as tta tta_model = tta.SegmentationTTAWrapper(model, tta.aliases.d4_transform(), merge_mode='mean') - For Classification Models:
tta_model = tta.ClassificationTTAWrapper(model, tta.aliases.five_crop_transform()) - For Keypoints Models:
tta_model = tta.KeypointsTTAWrapper(model, tta.aliases.flip_transform(), scaled=True)Note: Ensure your model returns keypoints in the format torch([x1, y1, …, xn, yn]).
Transforms
Transformations play a crucial role in augmenting the test images. Here’s a glimpse of the available transformations:
| Transform | Parameters | Values |
|---|---|---|
| HorizontalFlip | – | – |
| VerticalFlip | – | – |
| Rotate90 | angles | List[0, 90, 180, 270] |
| Scale | scales | List[float] |
| Multiply | factors | List[float] |
Aliases
The TTA library provides numerous aliases for convenience:
- flip_transform: Horizontal + Vertical Flips
- d4_transform: Flips + Rotations (0, 90, 180, 270)
- multiscale_transform: Variable scaling
- five_crop_transform: Center + Corner Crops
Merge Modes
After you’ve generated the predictions, it’s time to merge them using various merge modes. Here are the options:
- mean
- gmean (geometric mean)
- sum
- max
- min
- tsharpen (temperature sharpen)
Installation
To install TTAch, you can use either PyPI or the source:
- Using PyPI:
pip install ttach - From Source:
pip install git+https://github.com/qubvel/ttach
Troubleshooting
If you face any issues during installation or implementation, consider the following:
- Ensure that your model is correctly defined and matches the expected input/output formats.
- Check your Python and package versions for compatibility with TTAch.
- For debugging, adding print statements can help trace where things might be going wrong in your code.
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Conclusion
Whether you’re applying TTA to segmentation, classification, or keypoint tasks, it can significantly improve the performance of your models by enabling them to understand the test images better. Take your model to new heights by integrating Test Time Augmentation seamlessly!
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
