This is a study that I contributed to while working with the Microsoft AzureML AutoML team. My main contributions to this work are developing the end-to-end text-to-image retrieval example, performing pretrained and finetuning experiments for the text-to-image models, and adding support for the CLIP embeddings models to Azure Machine Learning catalog.

This is a feature that I contributed to while working with the Microsoft AzureML AutoML team. My main contributions to this work include service side support for training parameters, metrics reporting, transparent usage, multi-gpu finetuning, and telemetry reporting. My work also entailed adding support for pipeline sweeping on the SDK side. I also contributed significantly to testing coverage for services and SDK.

In this project, I explore a few common semantic segmentation techniques that have been used on weed/crop datasets for precision agriculture tasks. I use image patching as a data augmentation technique, train with transfer learning for three different semantic segmentation neural network architectures, and compare these models qualitatively and with the Intersection Over Union metric. I discuss the challenges and takeaways of the project.