In the field of computer vision, poor image quality in datasets poses significant challenges for engineers and data scientists, often leading to inaccurate models and extended project timelines due to misclassifications and incorrect feature learning. The article highlights a case study involving the Encord Active platform, which aids in identifying and improving low-quality images within datasets, using a “dog-food” image dataset from Hugging Face as an example. By employing Encord Active, users can explore, visualize, and rectify image quality issues through model-assisted quality metrics and one-click labeling integration, ultimately enhancing the dataset's quality and model performance. The platform facilitates the identification of problematic images, such as those with incorrect labels, blurriness, or poor brightness, and suggests strategies for rectification, including image augmentation and re-labeling. This systematic approach, which leverages tools like Hugging Face Datasets and Encord Index, emphasizes the importance of continuous evaluation and improvement of image quality to ensure robust computer vision models.