You have built computer vision applications that rely heavily on high-quality image data, but you've encountered challenges with bad-quality images in your datasets, such as mislabeled images, inconsistent resolutions, noise, and distortion. These issues can lead to models learning incorrect features, resulting in inaccurate or untrustworthy classifications and outputs. To enhance model effectiveness, it's essential to investigate, assess, and improve the quality of your image data. Encord Index offers a robust framework to pinpoint and label problematic images that refine the overall quality of your dataset. In this article, you'll explore how to use Encord Active to explore images, identify issues, and fix low-quality images within the Caltech101 dataset from the Torchvision Datasets library. You'll learn how to create an Encord Active project, compute image embeddings, analyze them with metrics, visualize aspect ratio score distributions, inspect problematic images, and take steps to rectify issues such as blurry or poorly lit images. By following this process, you can ensure that your dataset is of high quality, which is crucial for achieving superior model performance in computer vision applications.