Chatrex 7B

Developed by IDEA-Research

ChatRex is a perception-specialized multimodal large language model capable of associating answers with specific objects while responding to questions.

Image-to-Text

Safetensors

English#Fine-grained object perception #Multimodal dialogue #Region understanding

Downloads 825

Release Time : 11/25/2024

Model Overview

ChatRex is a multimodal large language model (MLLM) designed to seamlessly integrate fine-grained object perception with powerful language understanding capabilities. By adopting a decoupled architecture combined with a retrieval-based object detection approach and utilizing high-resolution visual inputs, ChatRex addresses key challenges in perception tasks.

Model Features

Fine-grained object perception

Capable of associating answers with specific objects in images, achieving fine-grained object perception.

Multimodal integration

Seamlessly integrates visual and language understanding capabilities, supporting various vision-language tasks.

High-resolution visual input

Utilizes high-resolution visual inputs to enhance the accuracy of perception tasks.

Universal Proposal Network (UPN)

Adopts a DETR architecture with dual-granularity prompt tuning strategy, combining fine-grained and coarse-grained detection capabilities.

Model Capabilities

Object detection

Entity-based dialogue

Entity-based image captioning

Region understanding

Multimodal QA

Use Cases

Visual QA

Object detection and entity association

Detects specific objects in images and associates answers with those objects.

Can accurately detect and associate objects in images.

Image captioning

Region description generation

Generates descriptions for specific regions in images.

Can produce accurate and detailed region descriptions.

Entity-based image captioning

Generates image captions containing entity indices.

Generated captions include indices for all mentioned objects.

Dialogue systems

Entity-based dialogue

Associates answers with specific objects in images during conversations.

Enables natural entity-based dialogues.

language:

en base_model:
lmsys/vicuna-7b-v1.5
openai/clip-vit-large-patch14
laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg pipeline_tag: image-text-to-text tags:
chatrex
upn

arxiv.org/abs/2411.18363

1. Introduction 📚

TL;DR: ChatRex is an MLLM skilled in perception that can respond to questions while simultaneously grounding its answers to the referenced objects.

ChatRex is a Multimodal Large Language Model (MLLM) designed to seamlessly integrate fine-grained object perception and robust language understanding. By adopting a decoupled architecture with a retrieval-based approach for object detection and leveraging high-resolution visual inputs, ChatRex addresses key challenges in perception tasks. It is powered by the Rexverse-2M dataset with diverse image-region-text annotations. ChatRex can be applied to various scenarios requiring fine-grained perception, such as object detection, grounded conversation, grounded image captioning and region understanding.

2. Installation 🛠️

conda install -n chatrex python=3.9
pip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu121
git clone https://github.com/IDEA-Research/ChatRex.git
cd ChatRex
pip install -v -e .
# install deformable attention for universal proposal network
cd chatrex/upn/ops
pip install -v -e .

2.1 Download Pre-trained UPN Models

We provide model checkpoints for both the Universal Proposal Network (UPN) and the ChatRex model. You can download the pre-trained models from the following links:

Or you can also using the following command to download the pre-trained models:

mkdir checkpoints
mkdir checkpoints/upn
# download UPN checkpoint
wget -O checkpoints/upn/upn_large.pth https://github.com/IDEA-Research/ChatRex/releases/download/upn-large/upn_large.pth

2.2 Verify Installation

To verify the installation of the Universal Proposal Network (UPN), run the following command:

python tests/test_upn_install.py

If the installation is successful, you will get two visualization images of both fine-grained proposal and coarse-grained proposal in tests folder.

To verify the installation of the ChatRex model, run the following command:

python tests/test_chatrex_install.py

If the installation is successful, you will get an output like this:

prediction: <obj0> shows a brown dog lying on a bed. The dog is resting comfortably, possibly sleeping, and is positioned on the left side of the bed

3. Usage 🚀

3.1 Use UPN for Object Proposal Generation

Universal Proposal Network (UPN) is a robust object proposal model designed as part of ChatRex to enable comprehensive and accurate object detection across diverse granularities and domains. Built upon T-Rex2, UPN is a DETR-based model with a dual-granularity prompt tuning strategy, combining fine-grained (e.g., part-level) and coarse-grained (e.g., instance-level) detection.

Example Code for UPN

import torch
from PIL import Image
from tools.visualize import plot_boxes_to_image
from chatrex.upn import UPNWrapper

ckpt_path = "checkpoints/upn_checkpoints/upn_large.pth"
test_image_path = "tests/images/test_upn.jpeg"

model = UPNWrapper(ckpt_path)
# fine-grained prompt
fine_grained_proposals = model.inference(
    test_image_path, prompt_type="fine_grained_prompt"
)
# filter by score (default: 0.3) and nms (default: 0.8)
fine_grained_filtered_proposals = model.filter(
    fine_grained_proposals, min_score=0.3, nms_value=0.8
)
## output is a dict with keys: "original_xyxy_boxes", "scores"
## - "original_xyxy_boxes": list of boxes in xyxy format in shape (B, N, 4)
## - "scores": list of scores for each box in shape (B, N)

# coarse-grained prompt
coarse_grained_proposals = model.inference(
    test_image_path, prompt_type="coarse_grained_prompt"
)
coarse_grained_filtered_proposals = model.filter(
    coarse_grained_proposals, min_score=0.3, nms_value=0.8
)

## output is a dict with keys: "original_xyxy_boxes", "scores"
## - "original_xyxy_boxes": list of boxes in xyxy format in shape (B, N, 4)
## - "scores": list of scores for each box in shape (B, N)

We also provide a visualization tool to visualize the object proposals generated by UPN. You can use the following code to visualize the object proposals:

Example Code for UPN Visualization


from chatrex.tools.visualize import plot_boxes_to_image
image = Image.open(test_image_path)
fine_grained_vis_image, _ = plot_boxes_to_image(
    image.copy(),
    fine_grained_filtered_proposals["original_xyxy_boxes"][0],
    fine_grained_filtered_proposals["scores"][0],
)
fine_grained_vis_image.save("tests/test_image_fine_grained.jpeg")
print(f"fine-grained proposal is saved at tests/test_image_fine_grained.jpeg")

coarse_grained_vis_image, _ = plot_boxes_to_image(
    image.copy(),
    coarse_grained_filtered_proposals["original_xyxy_boxes"][0],
    coarse_grained_filtered_proposals["scores"][0],
)
coarse_grained_vis_image.save("tests/test_image_coarse_grained.jpeg")
print(f"coarse-grained proposal is saved at tests/test_image_coarse_grained.jpeg")

3.2 Usage of ChatRex

ChatRex takes three inputs: image, text prompt, and box input. For the box input, you can either use the object proposals generated by UPN or provide your own box input (user drawn boxes). We have wrapped the ChatRex model to huggingface transformers format for easy usage. ChatRex can be used for various tasks and we provide example code for each task below.

3.2.1 ChatRex for Object Detection & Grounding & Referring

Example Prompt for detection, grounding, referring tasks:

# Single Object Detection
Please detect dog in this image. Answer the question with object indexes.
Please detect the man in yellow shirt in this image. Answer the question with object indexes.

# multiple object detection, use ; to separate the objects
Please detect person; pigeon in this image. Answer the question with object indexes.
Please detect person in the car; cat below the table in this image. Answer the question with object indexes.

Example Code

import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig

from chatrex.tools.visualize import visualize_chatrex_output
from chatrex.upn import UPNWrapper

if __name__ == "__main__":
    # load the processor
    processor = AutoProcessor.from_pretrained(
        "IDEA-Research/ChatRex-7B",
        trust_remote_code=True,
        device_map="cuda",
    )

    print(f"loading chatrex model...")
    # load chatrex model
    model = AutoModelForCausalLM.from_pretrained(
        "IDEA-Research/ChatRex-7B",
        trust_remote_code=True,
        use_safetensors=True,
    ).to("cuda")

    # load upn model
    print(f"loading upn model...")
    ckpt_path = "checkpoints/upn_checkpoints/upn_large.pth"
    model_upn = UPNWrapper(ckpt_path)
    test_image_path = "tests/images/test_chatrex_detection.jpg"

    # get upn predictions
    fine_grained_proposals = model_upn.inference(
        test_image_path, prompt_type="fine_grained_prompt"
    )
    fine_grained_filtered_proposals = model_upn.filter(
        fine_grained_proposals, min_score=0.3, nms_value=0.8
    )

    inputs = processor.process(
        image=Image.open(test_image_path),
        question="Please detect person; pigeon in this image. Answer the question with object indexes.",
        bbox=fine_grained_filtered_proposals["original_xyxy_boxes"][
            0
        ],  # box in xyxy format
    )

    inputs = {k: v.to("cuda") for k, v in inputs.items()}

    # perform inference
    gen_config = GenerationConfig(
        max_new_tokens=512,
        do_sample=False,
        eos_token_id=processor.tokenizer.eos_token_id,
        pad_token_id=(
            processor.tokenizer.pad_token_id
            if processor.tokenizer.pad_token_id is not None
            else processor.tokenizer.eos_token_id
        ),
    )
    with torch.autocast(device_type="cuda", enabled=True, dtype=torch.bfloat16):
        prediction = model.generate(
            inputs, gen_config=gen_config, tokenizer=processor.tokenizer
        )
    print(f"prediction:", prediction)

    # visualize the prediction
    vis_image = visualize_chatrex_output(
        Image.open(test_image_path),
        fine_grained_filtered_proposals["original_xyxy_boxes"][0],
        prediction,
        font_size=15,
        draw_width=5,
    )
    vis_image.save("tests/test_chatrex_detection.jpeg")
    print(f"prediction is saved at tests/test_chatrex_detection.jpeg")

The output from LLM is like:

<ground>person</ground><objects><obj10><obj14><obj15><obj27><obj28><obj32><obj33><obj35><obj38><obj47><obj50></objects>
<ground>pigeon</ground><objects><obj0><obj1><obj2><obj3><obj4><obj5><obj6><obj7><obj8><obj9><obj11><obj12><obj13><obj16><obj17><obj18><obj19><obj20><obj21><obj22><obj23><obj24><obj25><obj26><obj29><obj31><obj37><obj39><obj40><obj41><obj44><obj49></objects>

The visualization of the output is like:

3.2.2 ChatRex for Region Caption

Example Prompt for Region Caption tasks:

# Single Object Detection
## caption in category name
What is the category name of <obji>? Answer the question with its category name in free format.

## caption in short phrase
Can you provide me with a short phrase to describe <obji>? Answer the question with a short phrase.

## caption in referring style
Can you provide me with a brief description of <obji>? Answer the question with brief description.

## caption in one sentence
Can you provide me with a one sentence of <obji>? Answer the question with one sentence description.

# multiple object detection, use ; to separate the objects

Example Code

import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig

from chatrex.tools.visualize import visualize_chatrex_output
from chatrex.upn import UPNWrapper

if __name__ == "__main__":
    # load the processor
    processor = AutoProcessor.from_pretrained(
        "IDEA-Research/ChatRex-7B",
        trust_remote_code=True,
        device_map="cuda",
    )

    print(f"loading chatrex model...")
    # load chatrex model
    model = AutoModelForCausalLM.from_pretrained(
        "IDEA-Research/ChatRex-7B",
        trust_remote_code=True,
        use_safetensors=True,
    ).to("cuda")

    test_image_path = "tests/images/test_chatrex_install.jpg"

    inputs = processor.process(
        image=Image.open(test_image_path),
        question="Can you provide a one sentence description of <obj0> in the image? Answer the question with a one sentence description.",
        bbox=[[73.88417, 56.62228, 227.69223, 216.34338]],
    )

    inputs = {k: v.to("cuda") for k, v in inputs.items()}

    # perform inference
    gen_config = GenerationConfig(
        max_new_tokens=512,
        do_sample=False,
        eos_token_id=processor.tokenizer.eos_token_id,
        pad_token_id=(
            processor.tokenizer.pad_token_id
            if processor.tokenizer.pad_token_id is not None
            else processor.tokenizer.eos_token_id
        ),
    )
    with torch.autocast(device_type="cuda", enabled=True, dtype=torch.bfloat16):
        prediction = model.generate(
            inputs, gen_config=gen_config, tokenizer=processor.tokenizer
        )
    print(f"prediction:", prediction)

    # visualize the prediction
    vis_image = visualize_chatrex_output(
        Image.open(test_image_path),
        [[73.88417, 56.62228, 227.69223, 216.34338]],
        prediction,
        font_size=15,
        draw_width=5,
    )
    vis_image.save("tests/test_chatrex_region_caption.jpeg")
    print(f"prediction is saved at tests/test_chatrex_region_caption.jpeg")

The output from LLM is like:

<ground>A brown dog is lying on a bed, appearing relaxed and comfortable</ground><objects><obj0></objects>

The visualization of the output is like:

3.2.3 ChatRex for Grounded Image Captioning

Example Prompt for Region Caption tasks:

# Brief Grounded Imager Caption
Please breifly describe this image in one sentence and detect all the mentioned objects. Answer the question with grounded answer.

# Detailed Grounded Image Caption
Please provide a detailed description of the image and detect all the mentioned objects. Answer the question with grounded object indexes.

Example Code

import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig

from chatrex.tools.visualize import visualize_chatrex_output
from chatrex.upn import UPNWrapper

if __name__ == "__main__":
    # load the processor
    processor = AutoProcessor.from_pretrained(
        "IDEA-Research/ChatRex-7B",
        trust_remote_code=True,
        device_map="cuda",
    )

    print(f"loading chatrex model...")
    # load chatrex model
    model = AutoModelForCausalLM.from_pretrained(
        "IDEA-Research/ChatRex-7B",
        trust_remote_code=True,
        use_safetensors=True,
    ).to("cuda")

    # load upn model
    print(f"loading upn model...")
    ckpt_path = "checkpoints/upn_checkpoints/upn_large.pth"
    model_upn = UPNWrapper(ckpt_path)
    test_image_path = "tests/images/test_chatrex_grounded_caption.jpg"

    # get upn predictions
    fine_grained_proposals = model_upn.inference(
        test_image_path, prompt_type="fine_grained_prompt"
    )
    fine_grained_filtered_proposals = model_upn.filter(
        fine_grained_proposals, min_score=0.3, nms_value=0.8
    )

    inputs = processor.process(
        image=Image.open(test_image_path),
        question="Please breifly describe this image in one sentence and detect all the mentioned objects. Answer the question with grounded answer.",
        bbox=fine_grained_filtered_proposals["original_xyxy_boxes"][
            0
        ],  # box in xyxy format
    )

    inputs = {k: v.to("cuda") for k, v in inputs.items()}

    # perform inference
    gen_config = GenerationConfig(
        max_new_tokens=512,
        do_sample=False,
        eos_token_id=processor.tokenizer.eos_token_id,
        pad_token_id=(
            processor.tokenizer.pad_token_id
            if processor.tokenizer.pad_token_id is not None
            else processor.tokenizer.eos_token_id
        ),
    )
    with torch.autocast(device_type="cuda", enabled=True, dtype=torch.bfloat16):
        prediction = model.generate(
            inputs, gen_config=gen_config, tokenizer=processor.tokenizer
        )
    print(f"prediction:", prediction)

    # visualize the prediction
    vis_image = visualize_chatrex_output(
        Image.open(test_image_path),
        fine_grained_filtered_proposals["original_xyxy_boxes"][0],
        prediction,
        font_size=15,
        draw_width=5,
    )
    vis_image.save("tests/test_chatrex_grounded_image_caption.jpeg")
    print(f"prediction is saved at tests/test_chatrex_grounded_image_caption.jpeg")

The output from LLM is like:

The image depicts a cozy living room with a <ground>plaid couch,</ground><objects><obj2></objects> a <ground>wooden TV stand</ground><objects><obj3></objects>holding a <ground>black television,</ground><objects><obj1></objects> a <ground>red armchair,</ground><objects><obj4></objects> and a <ground>whiteboard</ground><objects><obj0></objects>with writing on the wall, accompanied by a <ground>framed poster</ground><objects><obj6></objects>of a <ground>couple.</ground><objects><obj9><obj11></objects>

The visualization of the output is like:

3.2.4 ChatRex for Grounded Conversation

Example Prompt for Region Caption tasks:

Answer the question in Grounded format. Question

Example Code

import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig

from chatrex.tools.visualize import visualize_chatrex_output
from chatrex.upn import UPNWrapper

if __name__ == "__main__":
    # load the processor
    processor = AutoProcessor.from_pretrained(
        "IDEA-Research/ChatRex-7B",
        trust_remote_code=True,
        device_map="cuda",
    )

    print(f"loading chatrex model...")
    # load chatrex model
    model = AutoModelForCausalLM.from_pretrained(
        "IDEA-Research/ChatRex-7B",
        trust_remote_code=True,
        use_safetensors=True,
    ).to("cuda")

    # load upn model
    print(f"loading upn model...")
    ckpt_path = "checkpoints/upn_checkpoints/upn_large.pth"
    model_upn = UPNWrapper(ckpt_path)
    test_image_path = "tests/images/test_grounded_conversation.jpg"

    # get upn predictions
    fine_grained_proposals = model_upn.inference(
        test_image_path, prompt_type="coarse_grained_prompt"
    )
    fine_grained_filtered_proposals = model_upn.filter(
        fine_grained_proposals, min_score=0.3, nms_value=0.8
    )

    inputs = processor.process(
        image=Image.open(test_image_path),
        question="Answer the question in grounded format. This is a photo of my room, and can you tell me what kind of person I am?  ",
        bbox=fine_grained_filtered_proposals["original_xyxy_boxes"][
            0
        ],  # box in xyxy format
    )

    inputs = {k: v.to("cuda") for k, v in inputs.items()}

    # perform inference
    gen_config = GenerationConfig(
        max_new_tokens=512,
        do_sample=False,
        eos_token_id=processor.tokenizer.eos_token_id,
        pad_token_id=(
            processor.tokenizer.pad_token_id
            if processor.tokenizer.pad_token_id is not None
            else processor.tokenizer.eos_token_id
        ),
    )
    with torch.autocast(device_type="cuda", enabled=True, dtype=torch.bfloat16):
        prediction = model.generate(
            inputs, gen_config=gen_config, tokenizer=processor.tokenizer
        )
    print(f"prediction:", prediction)

    # visualize the prediction
    vis_image = visualize_chatrex_output(
        Image.open(test_image_path),
        fine_grained_filtered_proposals["original_xyxy_boxes"][0],
        prediction,
        font_size=30,
        draw_width=10,
    )
    vis_image.save("tests/test_chatrex_grounded_conversation.jpeg")
    print(f"prediction is saved at tests/test_chatrex_grounded_conversation.jpeg")

The output from LLM is like:

Based on the items in the image, it can be inferred that the <ground>person</ground><objects><obj1></objects> who owns this room has an interest in fitness and possibly enjoys reading. The presence of the <ground>dumbbell</ground><objects><obj2></objects> suggests a commitment to physical activity, while the <ground>book</ground><objects><obj3></objects> indicates a liking for literature or reading. The <ground>sneaker</ground><objects><obj0></objects>s and the <ground>plush toy</ground><objects><obj1></objects> add a personal touch, suggesting that the <ground>person</ground><objects><obj1></objects> might also value comfort and perhaps has a playful or nostalgic side. However, without more context, it is not possible to accurately determine the individual's specific traits or <ground>person</ground><objects><obj1></objects>ality.

The visualization of the output is like:

5. LICENSE

ChatRex is licensed under the IDEA License 1.0, Copyright (c) IDEA. All Rights Reserved. Note that this project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses including but not limited to the:

OpenAI Terms of Use for the dataset.
For the LLM used in this project, the model is lmsys/vicuna-7b-v1.5, which is licensed under Llama 2 Community License Agreement.
For the high resolution vision encoder, we are using laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg which is licensed under MIT LICENSE.
For the low resolution vision encoder, we are using openai/clip-vit-large-patch14 which is licensed under MIT LICENSE

BibTeX 📚

@misc{jiang2024chatrextamingmultimodalllm,
      title={ChatRex: Taming Multimodal LLM for Joint Perception and Understanding}, 
      author={Qing Jiang and Gen Luo and Yuqin Yang and Yuda Xiong and Yihao Chen and Zhaoyang Zeng and Tianhe Ren and Lei Zhang},
      year={2024},
      eprint={2411.18363},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2411.18363}, 
}

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