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| license: apache-2.0 |
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| # ASM-FT Model Card |
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| ## Model details |
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| **Model type:** |
| ASM is a unified vision-language foundation model for open-world panoptic visual recognition and understanding. Aligning with LLMs, it supports versatile generation tasks, demonstrating impressive region comprehension capability. |
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| **Model date:** |
| ASM was trained in July 2023. |
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| **Paper or resources for more information:** |
| https://github.com/OpenGVLab/all-seeing |
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| ## License |
| ASM is open-sourced under the Apache License 2.0. |
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| **Where to send questions or comments about the model:** |
| https://github.com/OpenGVLab/all-seeing/issues |
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| ## Intended use |
| **Primary intended uses:** |
| The primary use of ASM is research on large multimodal models and chatbots. |
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| **Primary intended users:** |
| The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. |
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| ## Training dataset |
| The pretrain phase employs [AS-1B](https://huggingface.co/datasets/Weiyun1025/AS-100M/tree/main) and [Laion-COCO](https://huggingface.co/datasets/laion/laion-coco). |
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| The finetuning phase employs [AS-Core](https://huggingface.co/datasets/Weiyun1025/AS-Core), [RefCOCOg](https://github.com/lichengunc/refer), [VG](https://homes.cs.washington.edu/~ranjay/visualgenome/index.html), [LLaVA-150K](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K), [COCO Caption](https://cocodataset.org/#home), [TextCaps](https://textvqa.org/textcaps/), [VQAv2](https://visualqa.org/), and [GQA](https://cs.stanford.edu/people/dorarad/gqa/). |
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| ## Evaluation dataset |
| A collection of 4 benchmarks, including 2 image captioning benchmarks, and 2 region captioning benchmarks. |
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