TL;DR: ChatRex is an MLLM skilled in perception that can respond to questions while simultaneously grounding its answers to the referenced objects.
video_compressed.mp4
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.
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
pip install -v -e .
# install deformable attention for universal proposal network
cd chatrex/upn/ops
pip install -v -e .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
# Download ChatRex checkpoint from Hugging Face
git lfs install
git clone https://huggingface.co/IDEA-Research/ChatRex-7B checkpoints/chatrexTo verify the installation of the Universal Proposal Network (UPN), run the following command:
python tests/test_upn_install.pyIf 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.pyIf 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
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")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.
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:
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:
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:
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:
Here are Workflow Readme you can follow to run the gradio demos.
We provide a gradio demo for UPN to visualize the object proposals generated by UPN. You can run the following command to start the gradio demo:
python gradio_demos/upn_demo.py
# if there is permission error, please run the following command
mkdir tmp
TMPDIR='/tmp' python gradio_demos/upn_demo.py
We also provide a gradio demo for ChatRex. Before you use, we highly recommend you to watch the following video to understand how to use this demo:
Gradio_Demo_v2.mp4
python gradio_demos/chatrex_demo.py
# if there is permission error, please run the following command
mkdir tmp
TMPDIR='/tmp' python gradio_demos/upn_demo.py
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
@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},
}
