license: bsd-3-clause
language:
- en
pipeline_tag: visual-question-answering
library_name: transformers
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_FlanT5 was trained in July 2023.
Paper or resources for more information: https://gordonhu608.github.io/BLIVA/
License: BSD 3-Clause 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 FlanT5 is for commercial use on large multimodal models.
Primary intended users: The primary intended users of this model is for commercial companies 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