RegionGPT is a novel vision-language model designed to enhance region-level understanding and captioning capabilities. The model improves spatial awareness by modifying existing visual encoders in vision-language models (VLMs). It integrates task-guided instruction prompts during training and inference to improve performance on tasks requiring specific output scopes. Additionally, an automated region caption data generation pipeline is developed to enrich the training set with detailed region-level captions. The model is demonstrated to be effective across various region-level tasks, including complex region descriptions, reasoning, object classification, and referring expressions comprehension. The model's architecture includes a visual backbone, feature refinement module, and a large language model that processes both visual and text tokens. The model is trained on a diverse set of datasets, including V3Det, Visual Genome, and ReferCOCOg, and is evaluated on tasks such as region classification, captioning, and referring expression comprehension. The results show that RegionGPT significantly outperforms existing methods in these tasks, demonstrating its effectiveness in region-level understanding and captioning. The model is also evaluated on the object hallucination benchmark, where it performs better than recent image-level VLMs. The model's ability to handle complex region-level tasks is further demonstrated through qualitative evaluations, showing its capability to analyze relationships between multiple regions within an image. The model is implemented with a two-stage GPT-assisted annotation pipeline to generate detailed region-level captions. Overall, RegionGPT provides a robust framework for region-level vision-language understanding and is effective in various tasks requiring detailed visual comprehension.RegionGPT is a novel vision-language model designed to enhance region-level understanding and captioning capabilities. The model improves spatial awareness by modifying existing visual encoders in vision-language models (VLMs). It integrates task-guided instruction prompts during training and inference to improve performance on tasks requiring specific output scopes. Additionally, an automated region caption data generation pipeline is developed to enrich the training set with detailed region-level captions. The model is demonstrated to be effective across various region-level tasks, including complex region descriptions, reasoning, object classification, and referring expressions comprehension. The model's architecture includes a visual backbone, feature refinement module, and a large language model that processes both visual and text tokens. The model is trained on a diverse set of datasets, including V3Det, Visual Genome, and ReferCOCOg, and is evaluated on tasks such as region classification, captioning, and referring expression comprehension. The results show that RegionGPT significantly outperforms existing methods in these tasks, demonstrating its effectiveness in region-level understanding and captioning. The model is also evaluated on the object hallucination benchmark, where it performs better than recent image-level VLMs. The model's ability to handle complex region-level tasks is further demonstrated through qualitative evaluations, showing its capability to analyze relationships between multiple regions within an image. The model is implemented with a two-stage GPT-assisted annotation pipeline to generate detailed region-level captions. Overall, RegionGPT provides a robust framework for region-level vision-language understanding and is effective in various tasks requiring detailed visual comprehension.