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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: visual-question-answering |
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library_name: transformers |
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inference: false |
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--- |
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# BLIVA Model Card |
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## Model details |
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**Model type:** |
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BLIVA is an open-source Vision-Languagde model trained by initializing from InstructBLIP and alignment with Vicuna on multimodal instruction-finetuning data. |
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It composes of an EVA-CLIP vision encoder, a Q-Former, a projection layer and an auto-regressive language model, based on the decoder only transformer architecture. |
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**Model date:** |
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BLIVA_FlanT5 was trained in July 2023. |
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**Paper or resources for more information:** |
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https://gordonhu608.github.io/bliva/ |
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**License:** |
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Apache 2.0 License |
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**Where to send questions or comments about the model:** |
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https://github.com/mlpc-ucsd/BLIVA |
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## Intended use |
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**Primary intended uses:** |
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The primary use of BLIVA FlanT5 is for commercial use on large multimodal models. |
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**Primary intended users:** |
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The primary intended users of this model is for commercial companies in computer vision, natural language processing, machine learning, and artificial intelligence. |
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## Training dataset |
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Pre-train data: 558K filtered image-text pairs from LAION,CC-3M, and SBU. Selected by LLaVA. |
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Instruction-finetuning data: COCO-Caption, TextCaps, VQAv2, OKVQA, A-OKVQA, LLaVA-150K, OCR-VQA. |
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## Evaluation dataset |
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For zero-shot evaluation on general image task, we selected Nocaps, Flickr30K, VizWiz, Visual Spaial Reasoning (VSR), IconQA, Visual Dialog, ScienceQA, MSRVTT QA, TextVQA and Hateful Memes. |
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For zero-shot evaluation on text-rich image OCR task, we selected ST-VQA, OCR-VQA, Text-VQA, and Doc-VQA. |
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More detials are in our github, https://github.com/mlpc-ucsd/BLIVA |
