transformers pytorch blip-2 license:apache-2.0 endpoints_compatible region:us
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license: apache-2.0
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# ASM-FT Model Card

## Model details

**Model type:**
ASM is a unified vision-language foundation model for open-world panoptic visual recognition and understanding. Aligning with LLMs, it supports versatile generation tasks, demonstrating impressive region comprehension capability.

**Model date:**
ASM was trained in July 2023.

**Paper or resources for more information:**
https://github.com/OpenGVLab/all-seeing

## License
ASM is open-sourced under the Apache License 2.0.

**Where to send questions or comments about the model:**
https://github.com/OpenGVLab/all-seeing/issues

## Intended use
**Primary intended uses:**
The primary use of ASM is research on large multimodal models and chatbots.

**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
The pretrain phase employs [AS-1B](https://huggingface.co/datasets/Weiyun1025/AS-100M/tree/main) and [Laion-COCO](https://huggingface.co/datasets/laion/laion-coco).

The finetuning phase employs [AS-Core](https://huggingface.co/datasets/Weiyun1025/AS-Core), [RefCOCOg](https://github.com/lichengunc/refer), [VG](https://homes.cs.washington.edu/~ranjay/visualgenome/index.html), [LLaVA-150K](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K), [COCO Caption](https://cocodataset.org/#home), [TextCaps](https://textvqa.org/textcaps/), [VQAv2](https://visualqa.org/), and [GQA](https://cs.stanford.edu/people/dorarad/gqa/).

## Evaluation dataset
A collection of 4 benchmarks, including 2 image captioning benchmarks, and 2 region captioning benchmarks.
