AugmenTory: Revolutionizing Data Augmentation for Instance Segmentation
At Nik Fanavar Nasir Co., our mission goes beyond applying existing technologies; we are committed to building the infrastructure that powers the future of Artificial Intelligence. Today, we are proud to introduce AugmenTory, a proprietary open-source library developed by our R&D arm, Smartory Technology Lab, designed to solve one of the most persistent challenges in Computer Vision: efficient polygon augmentation.
The Challenge: The Bottleneck of Polygon Annotation
In the realm of modern AI, particularly in fields like medical imaging and autonomous systems, Instance Segmentation is critical. Unlike simple bounding boxes, segmentation requires precise "Polygons" to outline complex shapes—like a tooth in a dental X-ray or a tumor in an MRI.
However, expanding these datasets (Data Augmentation) has traditionally been computationally expensive. The standard approach involves converting polygons into heavy "Masks" (pixel-wise images), transforming them, and then converting them back. This process consumes massive amounts of memory and slows down training pipelines.
The Solution: AugmenTory
AugmenTory changes the game by treating polygon vertices as Keypoints rather than converting them to heavy masks. By directly applying transformations—such as rotation, flipping, and cropping—to these coordinates, our library achieves results that are exponentially faster and lighter than traditional methods.
Key Technical Breakthroughs
- Drastic Efficiency: In our benchmarks on the COCO dataset, AugmenTory required only 1.2% of the memory space and was significantly faster compared to conventional mask-based methods.
- Smart Thresholding: We implemented an intelligent post-processing feature that calculates the Intersection over Union (IoU). If a transformation (like a crop) cuts off too much of an object, AugmenTory automatically discards the label to prevent feeding "noisy" data to the model.
- Seamless Integration: The library is built to be flexible, easily integrating with popular frameworks like PyTorch and TensorFlow, and supports standard Albumentations transforms.
Usability: From an Internal Challenge to a Global Solution
Why did we build this tool? The story of AugmenTory began within the development process of the Smarteeth dental platform. During the initial design phases of our AI core, we encountered specific technical challenges regarding data augmentation that existing tools could not adequately address; so, we took matters into our own hands.
After developing and validating the module's success in an operational environment, our senior technical and research teams recommended sharing it. Recognizing the critical need within the global scientific and engineering community for such infrastructure, we decided not to keep this technology proprietary. Consequently, today AugmenTory is more than just a research paper; it has been released as a practical Open Source tool on GitHub for engineers worldwide to utilize.
Use Cases:
- Medical Imaging: Precise augmentation of dental or tissue samples without losing edge accuracy.
- Autonomous Navigation: Efficient processing of road obstacles and objects.
- Aerial Monitoring: Handling complex, irregular shapes in satellite imagery.
Alignment with Our Mission
The development of AugmenTory serves as a prime example of the core philosophy at Nik Fanavar Nasir Co.: "Developing Future Infrastructure."
We do not merely train models; we optimize the underlying machine learning pipelines. By creating tools like AugmenTory, Smartory Technology Lab bridges the gap between academic theory and industrial application, ensuring that High-Tech solutions are not only accurate but also scalable and optimized.
Read the full paper on arXiv: 2405.04442