# Detailed Image Captioning and Hashtag Generation
## 4 Experiments and Results
### Discussions and Limitations  
The results of this study demonstrate significant advancements in both detailed image captioning and hashtag generation, with important implications for AI-assisted content analysis and creation. The performance of the image captioning model, particularly its efficiency and high CAPTURE score, suggests that it is well suited for applications requiring detailed image descriptions in real-time scenarios. The model's ability to generate detailed captions quickly could be particularly valuable in improving web accessibility, aiding content creators, and enhancing image search capabilities. The fine-tuned Florence-2 model with the DOCCI adapter demonstrates significant improvements in generating detailed and contextually rich image captions. The CAPTURE metric results, in particular, highlight the model's ability to capture nuanced visual elements and their relationships within images. This improvement is crucial for applications requiring in-depth image understanding, such as content analysis for visually impaired users or detailed content indexing. The competitive performance against larger models like Llava-v1.6-mistral-7b-hf and Phi-3-vision-128k-instruct is particularly noteworthy, especially considering the proposed model's significantly lower computational requirements. This efficiency makes the approach more suitable for real-time applications and deployment on consumer-grade hardware, potentially enabling new applications for automated image understanding in everyday scenarios. For hashtag generation, the KeyBERT-based model shows promising results in terms of both accuracy and computational efficiency. The high precision indicates that the majority of generated hashtags are relevant, while the strong recall suggests that the model is effective at identifying a wide range of appropriate hashtags. The balanced F1-score demonstrates the model's overall effectiveness in generating relevant and descriptive hashtags. Compared to other methods, the KeyBERT implementation stands out for its balance of performance and speed. While YAKE offers faster processing, its lower precision and recall make it less suitable for applications where accuracy is crucial. The regular BERT approach, despite its popularity, fell significantly behind in all metrics, justifying the optimized KeyBERT approach. The Gemma-2-2B-it model, while showing moderate performance, is hampered by its long processing time, making it impractical for real-time applications. The model's fast processing time is particularly noteworthy as it enables real-time hashtag generation. This speed, combined with its accuracy, makes it well suited for integration into content creation workflows, allowing for the immediate suggestion of relevant hashtags as content is being produced. Figure 5: Introducing CapFlow. The combination of detailed image captioning and efficient hashtag generation presents significant opportunities for enhancing content analysis and creation workflows. Detailed captions and relevant hashtags can significantly improve the searchability and categorization of visual content across various platforms. The detailed image descriptions generated by the model can greatly improve the web browsing experience for visually impaired users, providing rich contextual information about image content. By automating the process of generating detailed image descriptions and relevant hashtags, these models can streamline content creation workflows, particularly for social media managers and digital marketers. The efficiency of the models makes them suitable for integration into various platforms, from web browsers to mobile applications, providing consistent functionality across different content management systems. However, there are limitations and areas for future research. While the image captioning model demonstrates strong performance, there is still room for improvement in handling complex scenes or rare objects.