BLIVA Vicuna

--- language: - en pipeline_tag: visual-question-answering library_name: transformers inference: false --- <br> <br> # BLIVA Model Card ## Model details **Model type:** BLIVA is an open-source Vision-Languagde model trained by initializing from InstructBLIP and alignment with Vicuna on multimodal instruction-finetuning data. 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. **Model date:** BLIVA_Vicuna was trained in July 2023. **Paper or resources for more information:** https://gordonhu608.github.io/bliva/ **License:** Non-commercial bespoke license **Where to send questions or comments about the model:** https://github.com/mlpc-ucsd/BLIVA ## Intended use **Primary intended uses:** The primary use of BLIVA is research on large multimodal models. **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. ## Training dataset Pre-train data: 558K filtered image-text pairs from LAION,CC-3M, and SBU. Selected by LLaVA. Instruction-finetuning data: COCO-Caption, TextCaps, VQAv2, OKVQA, A-OKVQA, LLaVA-150K, OCR-VQA. ## Evaluation dataset 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. For zero-shot evaluation on text-rich image OCR task, we selected ST-VQA, OCR-VQA, Text-VQA, and Doc-VQA. More detials are in our github, https://github.com/mlpc-ucsd/BLIVA

Source context: 0 downloads · 5 likes · Pipeline visual-question-answering · Library transformers · Repo mlpc-lab/BLIVA_Vicuna