Game-Changing Tools for Optimizing Data Quality and Performance in Computer Vision

With the recent focus on LLMs, it’s easy to forget how critical computer vision models have become. Applications of computer vision can be found across many industries, and the technology is still growing rapidly. As companies invest in computer vision, the technology is expected to continue to improve efficiency and drive innovation.

The healthcare industry stands to benefit with an estimated $2.57 billion market value by 2025, thanks to AI-powered diagnostic tools and wearables that detect diseases and health issues at an early stage. In a similar vein, the banking industry is keen to embrace facial recognition and mobile biometrics to streamline transactions, a market that will grow to $9.6 billion by 2022.

Healthcare and banking aren’t the only industries that stand to benefit from computer vision; the automobile industry plans to grow its autonomous vehicle market to $556.67 billion by 2026. Manufacturing processes are also getting a high-tech makeover with computer vision-enhanced quality control systems, and the retail industry is not far behind with computer vision-enhanced store layouts and checkout systems.

Modern computer vision applications rely on neural network models trained on vast amounts of images. Unfortunately, the efficient application of computer vision is hampered by inaccurate or mislabelled data, duplicate images, outliers, data leakage, and blurry images. When the startup Visual Layer scrutinized the popular Laion-1B dataset, an unsettling 105,000,000 duplicate images were discovered, along with numerous other quality issues.

To tackle these hurdles, Visual Layer recently unveiled two transformative tools: VL Profiler and VL Datasets. VL Profiler brings an unprecedented level of precision in identifying and addressing data quality issues, enabling the viewing of these issues and simultaneous corrective actions on multiple images. This leads to the elimination of redundant and low-quality images, improving the quality of the dataset and consequently, a model’s performance.

Meanwhile, VL Datasets is a pristine collection of computer vision datasets designed to minimize the issues listed above. As a free and open-source tool, VL Datasets provides an ideal starting point for training machine learning models on clean datasets.

The promise of computer vision is compelling, and the tools to make it a reality are readily available. Explore and integrate tools such as VL Profiler and VL Datasets into your workflow. They are indispensable for visualizing data and understanding the performance of your computer vision models. These tools enable teams to address the challenges in computer vision datasets and significantly elevate model performance, thus ushering AI applications with clear, computer-aided vision.

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