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license: apache-2.0 |
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# ASMv2 Model Card |
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## Model details |
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**Model type:** |
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ASMv2 is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on multimodal instruction-following data. |
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It integrates the Relation Conversation (ReC) ability while maintaining powerful general capabilities. |
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This model is also endowed with grounding and referring capabilities, exhibiting state-of-the-art performance on region-level tasks, and can be naturally adapted to the Scene Graph Generation task in an open-ended manner. |
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**Model date:** |
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ASMv2 was trained in January 2024. |
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**Paper or resources for more information:** |
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https://github.com/OpenGVLab/all-seeing |
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## License |
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ASMv2 is open-sourced under the Apache License 2.0. |
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**Where to send questions or comments about the model:** |
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https://github.com/OpenGVLab/all-seeing/issues |
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## Intended use |
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**Primary intended uses:** |
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The primary use of ASMv2 is research on large multimodal models and chatbots. |
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**Primary intended users:** |
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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 |
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The pretrain phase employs [5M filtered samples](https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/ccs_filtered.json) from CC12M, [10M filtered samples](https://huggingface.co/datasets/Weiyun1025/AS-V2/blob/main/as_pretrain_10m.json) from AS-1B, and 15M filtered samples from [GRiT](https://huggingface.co/datasets/zzliang/GRIT). |
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The instruction-tuning phase employs [4M samples](https://huggingface.co/datasets/Weiyun1025/AS-V2/blob/main/as_mix_4m.json) collected from a variety of sources, including image-level datasets |
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See [here](https://github.com/OpenGVLab/all-seeing/tree/main/all-seeing-v2#training) for more details. |
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## Evaluation dataset |
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A collection of 20 benchmarks, including 5 academic VQA benchmarks, 7 multimodal benchmarks specifically proposed for instruction-following LMMs, 3 referring expression comprehension benchmarks, 2 region captioning benchmarks, 1 referring question answering benchmark, 1 scene graph generation benchmark, and 1 relation comprehension benchmark. |
