A developer recently shared their frustrating experience on Reddit detailing the significant data quality challenges faced while attempting to fine-tune a vision model on a product catalog dataset. The project was plagued by issues including missing and corrupted images, product descriptions riddled with inconsistent formatting like HTML tags and Unicode errors, and wildly varying image sizes that led to memory management problems. The developer, posting in the r/artificial subreddit, questioned whether this level of extensive data cleaning and preparation is typical in applied AI and machine learning projects. The discussion highlights the often-underestimated importance of data quality in the success of vision model training.
Vision Model Fine-Tuning Hampered by Data Quality Nightmares
