File size: 2,182 Bytes
0d77b4c 16e3177 dc06175 16e3177 f43eae1 16e3177 1a837c8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | ---
license: apache-2.0
---
# ASMv2 Model Card
## Model details
**Model type:**
ASMv2 is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on multimodal instruction-following data.
It integrates the Relation Conversation (ReC) ability while maintaining powerful general capabilities.
This model is also endowed with grounding and referring capabilities, exhibiting state-of-the-art performance on region-level tasks, and can be naturally adapted to the Scene Graph Generation task in an open-ended manner.
**Model date:**
ASMv2 was trained in January 2024.
**Paper or resources for more information:**
https://github.com/OpenGVLab/all-seeing
## License
ASMv2 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 ASMv2 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 [5M filtered samples](https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/ccs_filtered.json) from CC12M, [10M filtered samples](https://huggingface.co/datasets/Weiyun1025/AS-V2/blob/main/as_pretrain_10m.json) from AS-1B, and 15M filtered samples from [GRiT](https://huggingface.co/datasets/zzliang/GRIT).
The instruction-tuning phase employs [4M samples](https://huggingface.co/datasets/Weiyun1025/AS-V2/blob/main/as_mix_4m.json) collected from a variety of sources, including image-level datasets
See [here](https://github.com/OpenGVLab/all-seeing/tree/main/all-seeing-v2#training) for more details.
## Evaluation dataset
A collection of 20 benchmarks, including 5 academic VQA benchmarks, 7 multimodal benchmarks specifically proposed for instruction-following LMMs, 3 referring expression comprehension benchmarks, 2 region captioning benchmarks, 1 referring question answering benchmark, 1 scene graph generation benchmark, and 1 relation comprehension benchmark. |