Tuning Data Augmentation to Boost Model Performance
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Tuning Data Augmentation to Boost Model Performance
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In this webinar, SigOpt ML Engineer Meghana Ravikumar presents on and builds an image classifier trained on the Stanford Cars dataset to evaluate two approaches to transfer learning—fine tuning and feature extraction—and the impact of Multitask optimization, a more efficient form of Bayesian optimization, on these techniques. Once we define the most performant transfer learning technique for Stanford Cars, we will use image augmentation to double the size of the dataset to boost the classifier’s performance. Instead of manually tuning the hyperparameters associated with image augmentation, we will use Multitask Optimization to learn these hyperparameters using the downstream image classifier’s performance as the guide. In conjunction with model performance, we will also explore the features of these augmented images and the downstream implications for our image classifier.
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Tuning Data Augmentation to Boost Model Performance
- ImageNet Pretrained Convolutional Layers Fully Connected Layer ImageNet Pretrained Convolutional Layers Fully Connected Layer Input Convolutional Features Classification Input Convolutional Features Classification
- ImageNet Pretrained Convolutional Layers Fully Connected Layer Car Image Data Convolutional Features Classification
- Partial Full
- Acura TLX 2015 https://arxiv.org/abs/1512.03385
- Low Cost 10% of epochs Medium Cost 50% of epochs Full Cost 100% of epochs
- ImageNet Pretrained Convolutional Layers Fully Connected Layer ImageNet Pretrained Convolutional Layers Fully Connected Layer Input Convolutional Features Classification Input Convolutional Features Classification
- ImageNet Pretrained Convolutional Layers Fully Connected Layer Car Image Data Convolutional Features Classification
- Stanford Cars Dataset Image Augmentation Model Training (ResNet 18 fine-tuned) Model Accuracy SigOpt Model Validation Accuracy Suggested Hyperparameters Suggested Hyperparameters
- ImageNet Pretrained Convolutional Layers Fully Connected Layer Input Convolutional Features Classification
- ImageNet Pretrained Convolutional Layers Fully Connected Layer Input Convolutional Features Classification Cars Image Data Image Augmentation Accuracy