Using Multimodal Large Language Models for Photo Analysis - Bubnov, Mykhailo; Kliegr, Tomáš; Pavlíček, Luboš; Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky (2025)
This thesis investigates the potential of multimodal large language models to be used as a tool for the official automated passport photo classification, an area where high accuracy is demanded. The publicly available MMLLM (Multi-Modal Large Language Model) LLaVa v1.6 Vicuna 13B model was used and adapted using different tactics. The primary objective was to create a model that could classify passport-style photographs according to criteria defined by an international standard. A manually curated dataset was created to serve both as a proof-of-concept and as training material for fine-tuning. In the process of fine-tuning, the learning rate and the number of epochs were systematically varied to achieve the best possible result. The fine-tuned model achieved both F1 and AUC of 0.77. These results demonstrate measurable improvements over the base model. Further improvements are suggested through enhanced dataset diversity and more granular supervision.
Vedoucí práce: Tomáš Kliegr
Bakalářská práce (Bc.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2025
Obsahuje bibliografii
Textový (vysokoškolská kvalifikační práce)
Rok obhajoby 2025
Způsob přístupu: Internet
Keywords: aplikovaná informatika [obor bakal. práce]; bakalářské práce; bachelor's theses