DeepRoom: A Deep Learning Rating System for Photography

This thesis explores integrating deep learning techniques into photography, aiming to automate the identification of good images within large datasets. The primary focus is developing a deep learning-based system called DeepRoom that rates and evaluates photographs based on photography-specific technical criteria. To accomplish this, the research methodology encompasses qualitative research alongside developing a system prototype. A section overviews deep learning, photography, and related work and emphasizes its relevance to the research objectives. Implementation details include describing development tools and processes employed to construct the deep learning models and curate the dataset. These models' performance is assessed in the following evaluation phase, and a comparative analysis is conducted against existing software solutions. Encouraging results are observed, particularly in object detection and exposure classification, while identifying areas for improvement, such as refining the blurry and skewed horizon models. In conclusion, this research highlights the contributions of DeepRoom and proposes future work, including dataset expansion and model refinement, to enhance its capabilities further