Rex-Omni is a 3B-parameter Multimodal Large Language Model (MLLM) that redefines object detection and a wide range of other visual perception tasks as a simple next-token prediction problem.

• [2025-10-31] We release the AWQ quantized version of Rex-Omni, which saves 50% of the storage space. Rex-Omni-AWQ
• [2025-10-29] Fine-tuning code is now available.
• [2025-10-17] Evaluation code and dataset is now available.
• [2025-10-15] Rex-Omni is released.

• News 🎉
• Table of Contents
  • TODO LIST 📝
  • 1. Installation ⛳️
  • 2. Quick Start: Using Rex-Omni for Detection
    • Initialization parameters (RexOmniWrapper)
    • Inference parameters (rex.inference)
  • 3. Cookbooks
  • 4. Applications of Rex-Omni
  • 5. Gradio Demo
    • Quick Start
    • Available Options
  • 6. Evaluation
  • 7. Fine-tuning Rex-Omni
  • 8. LICENSE
  • 9. Citation

• Add Evaluation Code
• Add Fine-tuning Code
• Add Quantilized Rex-Omni

git clone https://github.com/IDEA-Research/Rex-Omni.git
cd Rex-Omni
conda create -n rexomni python=3.10 -y
conda activate rexomni
pip install torch==2.7.0 torchvision --index-url https://download.pytorch.org/whl/cu128
pip install -r requirements.txt

Test Installation

CUDA_VISIBLE_DEVICES=1 python tutorials/detection_example/detection_example.py

If the installation is successful, you will find a visualization of the detection results at tutorials/detection_example/test_images/cafe_visualize.jpg

Below is a minimal example showing how to run object detection using the rex_omni package.

from PIL import Image
from rex_omni import RexOmniWrapper, RexOmniVisualize

# 1) Initialize the wrapper (model loads internally)
rex = RexOmniWrapper(
    model_path="IDEA-Research/Rex-Omni",   # HF repo or local path
    backend="transformers",                # or "vllm" for high-throughput inference
    # Inference/generation controls (applied across backends)
    max_tokens=2048,
    temperature=0.0,
    top_p=0.05,
    top_k=1,
    repetition_penalty=1.05,
)

# If you are using the AWQ quantized version of Rex-Omni, you can use the following code:
rex = RexOmniWrapper(
    model_path="IDEA-Research/Rex-Omni-AWQ",
    backend="vllm",
    quantization="awq",
    max_tokens=2048,
    temperature=0.0,
    top_p=0.05,
    top_k=1,
    repetition_penalty=1.05,
)

# 2) Prepare input
image = Image.open("tutorials/detection_example/test_images/cafe.jpg").convert("RGB")
categories = [
    "man", "woman", "yellow flower", "sofa", "robot-shope light",
    "blanket", "microwave", "laptop", "cup", "white chair", "lamp",
]

# 3) Run detection
results = rex.inference(images=image, task="detection", categories=categories)
result = results[0]

# 4) Visualize
vis = RexOmniVisualize(
    image=image,
    predictions=result["extracted_predictions"],
    font_size=20,
    draw_width=5,
    show_labels=True,
)
vis.save("tutorials/detection_example/test_images/cafe_visualize.jpg")

• model_path: Hugging Face repo ID or a local checkpoint directory for the Rexe-Omni model.
• backend: "transformers" or "vllm".
  • transformers: easy to use, good baseline latency.
  • vllm: high-throughput, low-latency inference. Requires the vllm package and a compatible environment.
• max_tokens: Maximum number of tokens to generate for each output.
• temperature: Sampling temperature; higher values increase randomness (0.0 = deterministic/greedy).
• top_p: Nucleus sampling parameter; model samples from the smallest set of tokens with cumulative probability ≥ top_p.
• top_k: Top-k sampling; restricts sampling to the k most likely tokens.
• repetition_penalty: Penalizes repeated tokens; >1.0 discourages repetition.
• Optional advanced settings (supported via kwargs when constructing the wrapper):
  • Transformers: torch_dtype, attn_implementation, device_map, trust_remote_code, etc.
  • VLLM: tokenizer_mode, limit_mm_per_prompt, max_model_len, gpu_memory_utilization, tensor_parallel_size, trust_remote_code, etc.

• images: A single PIL.Image.Image or a list of images for batch inference.
• task: One of "detection", "pointing", "visual_prompting", "keypoint", "ocr_box", "ocr_polygon", "gui_grounding", "gui_pointing".
• categories: List of category names/phrases to detect or extract, e.g., ["person", "cup"]. Used to build task prompts.
• **keypoint_type": Type of keypoints for keypoint detection task. Options: "person", "hand", "animal"
• visual_prompt_boxes: Reference bounding boxes for visual prompting task. Format: [[x0, y0, x1, y1], ...] in absolute coordinates

Returns a list of dictionaries (one per input image). Each dictionary includes:

• raw_output: The raw text generated by the LLM.
• extracted_predictions: Structured predictions parsed from the raw output, grouped by category.
  • For detection: {category: [{"type": "box", "coords": [x0,y0,x1,y1]}, ...], ...}
  • For pointing: {category: [{"type": "point", "coords": [x0,y0]}, ...], ...}
  • For polygon: {category: [{"type": "polygon", "coords": [x0,y0, ...]}, ...], ...}
  • For keypointing: Structured Json

Tips:

• For best performance with VLLM, set backend="vllm" and tune gpu_memory_utilization and tensor_parallel_size according to your GPUs.

We provide comprehensive tutorials for each supported task. Each tutorial includes both standalone Python scripts and interactive Jupyter notebooks.

Task	Applications	Demo	Python Example	Notebook
Detection	object detection		code	notebook
	object referring		code	notebook
	gui grounding		code	notebook
	layout grounding		code	notebook
Pointing	object pointing		code	notebook
	gui pointing		code	notebook
	affordance pointing		code	notebook
Visual prompting	visual prompting		code	notebook
OCR	ocr word box		code	notebook
	ocr textline box		code	notebook
	ocr polygon		code	notebook
Keypointing	person keypointing		code	notebook
	animal keypointing		code	notebook
Other	batch inference		code

Rex-Omni's unified detection framework enables seamless integration with other vision models.

Application	Description	Demo	Documentation
Rex-Omni + SAM	Combine language-driven detection with pixel-perfect segmentation. Rex-Omni detects objects → SAM generates precise masks		README
Grounding Data Engine	Automatically generate phrase grounding annotations from image captions using spaCy and Rex-Omni.		README

We provide an interactive Gradio demo that allows you to test all Rex-Omni capabilities through a web interface.

# Launch the demo
CUDA_VISIBLE_DEVICES=0 python app.py --model_path IDEA-Research/Rex-Omni

# With custom settings
CUDA_VISIBLE_DEVICES=0 python app.py \
    --model_path IDEA-Research/Rex-Omni \
    --backend vllm \
    --server_name 0.0.0.0 \
    --server_port 7890

• --model_path: Model path or HuggingFace repo ID (default: "IDEA-Research/Rex-Omni")
• --backend: Backend to use - "transformers" or "vllm" (default: "transformers")
• --server_name: Server host address (default: "192.168.81.138")
• --server_port: Server port (default: 5211)
• --temperature: Sampling temperature (default: 0.0)
• --top_p: Nucleus sampling parameter (default: 0.05)
• --max_tokens: Maximum tokens to generate (default: 2048)

Please refer to Evaluation for more details.

Please refer to Fine-tuning Rex-Omni for more details.

Rex-Omni is licensed under the IDEA License 1.0, Copyright (c) IDEA. All Rights Reserved. This model is based on Qwen, which is licensed under the Qwen RESEARCH LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.

Rex-Omni comes from a series of prior works. If you’re interested, you can take a look.

• RexThinker
• RexSeek
• ChatRex
• DINO-X
• Grounidng DINO 1.5
• T-Rex2
• T-Rex

@misc{jiang2025detectpointprediction,
      title={Detect Anything via Next Point Prediction}, 
      author={Qing Jiang and Junan Huo and Xingyu Chen and Yuda Xiong and Zhaoyang Zeng and Yihao Chen and Tianhe Ren and Junzhi Yu and Lei Zhang},
      year={2025},
      eprint={2510.12798},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2510.12798}, 
}