mlpc-lab/BLIVA_Vicuna

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