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NExT-Chat

NExT-Chat: An LMM for Chat, Detection and Segmentation

Ao Zhang, Yuan Yao, Wei Ji, Zhiyuan Liu, and Tat-Seng Chua

National University of Singapore, Tsinghua University

Project page with demo: NExT-Chat

What's New: 🎉

2023.2.5 Add deepspeed training
2023.12.12 Initial code released

Introduction
Installation
Model Zoo
Data Preparation
Demo
Evaluation
Training
Examples
Acknowledgement

Introduction

An LMM for chat with detection and segmentation results. The framework is shown:

Installation

Please clone the repo:

git clone https://github.com/NExT-ChatV/NExT-Chat.git
cd NExT-Chat

Then install requirements:

pip install -r requirements.txt

Model Zoo

Currently, we totally have 3 models:

Version	ckpt	LM Size	ViT Res.	GPU Mem.	Comment
v1	nextchat-7b-336	7B	336x336	~32G	recommended
v0	nextchat-7b-224	7B	224x224	~24G	not recommended
v0	nextchat-13b-224	7B	224x224	~35G	not recommended

We recommend to use the nextchat-7b-336-v1, which can achieve better performance. Moreover, we also update the training templates for nextchat-7b-336-v1 to make it easier to use. You can refer to templates for details in eliciting concrete abilities. Some examples:

Localize a object:

Version	Template
v0	Where is XXX in the <image>?
v1	Where is XXX in the image?

Grounded Caption:

Version	Template
v0	Can you provide a description of the image <image> and include the locations for each mentioned object?
v1	Can you describe the image and include object locations?

VQA+Localization

Version	Template
v0	<Question> Please include object locations and explain.
v1	<Question> Please mention related object locations.

Data Preparation

Please refer to DATA.md.

Demo

Please first download the model weights from huggingface or our link. We also use OpenAI CLIP ViT model as the visual encoder. Please make sure that you can connect to huggingface or you can download it to your local directory. Then, download the SAM and modify sam_path in config/base/model/nextchat.py to your sam path.

Web Demo Please run:

CUDA_VISIBLE_DEVICES="0" python mllm/demo/web_demo.py --model_path path/to/model_weights --vit_path path/to/openai-clip-vit-large-patch14-336

If you can connect to huggingface, just run:

CUDA_VISIBLE_DEVICES="0" python mllm/demo/web_demo.py --model_path AoZhang/nextchat-7b-336 --vit_path openai/clip-vit-large-patch14-336

Bash Demo

CUDA_VISIBLE_DEVICES="0" python mllm/demo/bash_demo.py path/to/model_weights  path/to/openai-clip-vit-large-patch14-336

If you can connect to huggingface, just run:

CUDA_VISIBLE_DEVICES="0" python mllm/demo/bash_demo.py AoZhang/nextchat-7b-336  openai/clip-vit-large-patch14-336

You can also initialize the model by yourself:

from mllm.demo.demo_util import NextChatInference
model = NextChatInference(model_weight_path, vit_path, 576)

You will get into the IPython mode. Then use the model like:

input = {"text": "What is the possible relationship between the two people? Please include object locations.", "image": "./COCO_val2014_000000222628.jpg"}
response, boxes, masks, ret_img = model(input)

Easy Run

We have our old models (v0 versions) in the modelscope. Please first install pip install modelscope. Then run:

from modelscope import pipeline
pipe = pipeline('my-nextchat-task', 'ZhangAo6/nextchat', model_size="7b") # 7b model takes around 21G GPU mem, 13b takes around 35G GPU mem
response, ret_image = pipe({"text": "xxxx?", "image": "xxx.jpg"})
# response: the text answer
# ret_image: image annotated with boxes and masks

Evaluation

The final result have not been updated to the arxiv. We show the results here:

Referring Expression Segmentation (RES)

Please config the vision_tower in the config/base/model/nextchat.py to the path of OpenAI CLIP, if you can not connect to huggingface. Make sure to download the SAM and modify sam_path in config/base/model/nextchat.py to your sam path.

bash eval_res.sh /path/to/checkpoint

Referring Expression Comprehension (REC)

Although it seems to be better by modeling the localization as a regression task (also validated by toy experiments), we find that pixel2emb now is hard to beat top-tier pixel2seq models on REC (like Shikra) in the pre-training setting. We guess the key factors might be to find a balance between the localization loss and LM loss, which will significantly affect the REC performance. We are still working on this interesting finding and tune the model. If you have some insights, welcome to discuss.

bash eval_rec.sh /path/to/checkpoint

Pope (Image-level Hallucination)

bash eval_pope.sh /path/to/checkpoint

RefCOCOg-google (Region Caption)

bash eval_reg_cap.sh /path/to/checkpoint

Training

Our training consists of 3 stages:

VL+Detection Pre-training

bash run_stage1.sh

If you want to use deepspeed, please modify the config in run_stage1.sh from config/nextchat_stage1.py to config/nextchat_stage1_deepspeed.py.

VL+Detection Instruction Following

bash run_stage2.sh output/stage1/checkpoint-65000 # or other path to your stage1 model, we use 65000 for stage2

If you want to use deepspeed, please modify the config in run_stage2.sh from config/nextchat_stage2.py to config/nextchat_stage2_deepspeed.py.

VL+Detection+Segmentation

bash run_stage3.sh output/stage2/checkpoint-4950 # or other path to your stage2 model

If you want to use deepspeed, please modify the config in run_stage3.sh from config/nextchat_stage3.py to config/nextchat_stage3_deepspeed.py.

Examples

Examples generated by our nextchat-13b-v0 models:

Acknowledgement

Thanks to Shikra, LLaVA, CogVLM for their excellent codes.

Our bibtex:

@misc{zhang2023nextchat,
      title={NExT-Chat: An LMM for Chat, Detection and Segmentation}, 
      author={Ao Zhang and Yuan Yao and Wei Ji and Zhiyuan Liu and Tat-Seng Chua},
      year={2023},
      eprint={2311.04498},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}