Data Augmentation Tool

Techsolut's data augmentation tool allows you to enrich your image datasets by automatically generating realistic variations from your existing images. This technique is essential for improving the robustness and performance of your computer vision models.

Principle and Benefits

Data augmentation involves creating new training images by applying various transformations to original images. This approach offers several advantages:

• Increase dataset size without additional collection
• Improve model generalization
• Reduce overfitting
• Better robustness to natural variations (lighting, orientation, etc.)
• Compensate for imbalances in classes

Available Transformations

Our tool offers a wide range of transformations:

Geometric Transformations

• Rotation (from 0° to 360°)
• Horizontal and vertical flipping
• Translation
• Scaling (zoom in/out)
• Shearing
• Elastic deformation
• Perspective

Color Transformations

• Brightness adjustment
• Contrast adjustment
• Saturation adjustment
• Hue adjustment
• White balance
• Weather condition simulation

Special Transformations

• Gaussian blur
• Noise (Gaussian, salt and pepper, etc.)
• Partial occlusion (area masking)
• MixUp (image fusion)
• CutMix (cutting and pasting)
• Mosaic (image assembly)

User Interface

The intuitive interface allows you to:

1. Select your images - Import a folder of images or select an existing dataset
2. Configure transformations - Choose which transformations to apply and their parameters
3. Preview results - See generated images before validating
4. Generate and export - Create the new augmented dataset

Usage Modes

Manual Mode

Precisely configure each transformation and visualize the effect on your images.

Assisted Mode

The tool suggests transformations adapted to your data type and task.

Automatic Mode

Simply define the number of desired images and let the tool generate a balanced dataset.

Annotation Management

The tool intelligently manages your image annotations:

• Bounding boxes - Automatically adjusted during transformations
• Segmentation masks - Transformed along with the image
• Keypoints - Correctly repositioned
• Classifications - Preserved during augmentation

Quality Control

To ensure the quality of augmented data:

• Automatic filtering of poor quality images
• Artifact detection
• Annotation consistency verification
• Batch validation with rejection possibility

Workflow Integration

Augmentation can be:
- Performed offline (pre-generation)
- Integrated directly into the training pipeline (on-the-fly augmentation)
- Dynamically adapted based on model performance