Augmentor

Create a pipeline with rotations, zooms, and weather-simulating augmentations (e.g., brightness adjustments, Gaussian noise).

Apply perspective transforms to simulate different camera angles.

Generate augmented images and feed them into the training process.

Evaluate the model's performance on a held-out test set.

Iterate on the augmentation pipeline to improve generalization.

Augment medical images with rotations, flips, and elastic distortions.

Augment ground truth data (tumor masks) in parallel with original images.

Train the model on the augmented dataset.

Validate the model's performance on a separate validation set.

Refine the augmentation strategy based on model performance.

Use augmentations to simulate varying lighting conditions (brightness, contrast).

Apply perspective transforms and rotations to simulate different head poses.

Augment the dataset and train the facial recognition model.

Test the model on a challenging dataset with diverse lighting and pose variations.

Fine-tune the augmentation pipeline as needed.

Apply augmentations such as rotations, zooms, and flips to product images.

Simulate occlusions by adding random patches or objects to the images.

Augment the training dataset and train the object detection model.

Evaluate the model's ability to recognize products under varying conditions.

Optimize the augmentation pipeline based on the model's performance.