Rex-Omni

71.9K
33
2 languages
—
by
IDEA-Research
Image Model
OTHER
Fair
72K downloads
Community-tested
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Quick Summary

This model is Rex-Omni, a 3B-parameter Multimodal Large Language Model (MLLM) presented in the paper "Detect Anything via Next Point Prediction".

Code Examples

🚀 Quick Startbash
conda create -n rexomni -m python=3.10
pip install torch==2.6.0 torchvision==0.21.0 --index-url https://download.pytorch.org/whl/cu124
git clone https://github.com/IDEA-Research/Rex-Omni.git
cd Rex-Omni
pip install -v -e .
🚀 Quick Startbash
conda create -n rexomni -m python=3.10
pip install torch==2.6.0 torchvision==0.21.0 --index-url https://download.pytorch.org/whl/cu124
git clone https://github.com/IDEA-Research/Rex-Omni.git
cd Rex-Omni
pip install -v -e .
🚀 Quick Startbash
conda create -n rexomni -m python=3.10
pip install torch==2.6.0 torchvision==0.21.0 --index-url https://download.pytorch.org/whl/cu124
git clone https://github.com/IDEA-Research/Rex-Omni.git
cd Rex-Omni
pip install -v -e .
🚀 Quick Startbash
conda create -n rexomni -m python=3.10
pip install torch==2.6.0 torchvision==0.21.0 --index-url https://download.pytorch.org/whl/cu124
git clone https://github.com/IDEA-Research/Rex-Omni.git
cd Rex-Omni
pip install -v -e .
🚀 Quick Startbash
conda create -n rexomni -m python=3.10
pip install torch==2.6.0 torchvision==0.21.0 --index-url https://download.pytorch.org/whl/cu124
git clone https://github.com/IDEA-Research/Rex-Omni.git
cd Rex-Omni
pip install -v -e .
🚀 Quick Startbash
conda create -n rexomni -m python=3.10
pip install torch==2.6.0 torchvision==0.21.0 --index-url https://download.pytorch.org/whl/cu124
git clone https://github.com/IDEA-Research/Rex-Omni.git
cd Rex-Omni
pip install -v -e .
🚀 Quick Startbash
conda create -n rexomni -m python=3.10
pip install torch==2.6.0 torchvision==0.21.0 --index-url https://download.pytorch.org/whl/cu124
git clone https://github.com/IDEA-Research/Rex-Omni.git
cd Rex-Omni
pip install -v -e .
2. Quick Start: Using Rex-Omni for Detectionpythonvllm
from PIL import Image
from rex_omni import RexOmniWrapper, RexOmniVisualize

# Initialize model
model = RexOmniWrapper(
    model_path="IDEA-Research/Rex-Omni",
    backend="transformers"  # or "vllm"
)

# Load image
image = Image.open("your_image.jpg")

# Object Detection
results = model.inference(
    images=image,
    task="detection",
    categories=["person", "car", "dog"]
)

result = results[0]

# 4) Visualize
vis = RexOmniVisualize(
    image=image,
    predictions=result["extracted_predictions"],
    font_size=20,
    draw_width=5,
    show_labels=True,
)
vis.save("visualize.jpg")
2. Quick Start: Using Rex-Omni for Detectionpythonvllm
from PIL import Image
from rex_omni import RexOmniWrapper, RexOmniVisualize

# Initialize model
model = RexOmniWrapper(
    model_path="IDEA-Research/Rex-Omni",
    backend="transformers"  # or "vllm"
)

# Load image
image = Image.open("your_image.jpg")

# Object Detection
results = model.inference(
    images=image,
    task="detection",
    categories=["person", "car", "dog"]
)

result = results[0]

# 4) Visualize
vis = RexOmniVisualize(
    image=image,
    predictions=result["extracted_predictions"],
    font_size=20,
    draw_width=5,
    show_labels=True,
)
vis.save("visualize.jpg")
2. Quick Start: Using Rex-Omni for Detectionpythonvllm
from PIL import Image
from rex_omni import RexOmniWrapper, RexOmniVisualize

# Initialize model
model = RexOmniWrapper(
    model_path="IDEA-Research/Rex-Omni",
    backend="transformers"  # or "vllm"
)

# Load image
image = Image.open("your_image.jpg")

# Object Detection
results = model.inference(
    images=image,
    task="detection",
    categories=["person", "car", "dog"]
)

result = results[0]

# 4) Visualize
vis = RexOmniVisualize(
    image=image,
    predictions=result["extracted_predictions"],
    font_size=20,
    draw_width=5,
    show_labels=True,
)
vis.save("visualize.jpg")
2. Quick Start: Using Rex-Omni for Detectionpythonvllm
from PIL import Image
from rex_omni import RexOmniWrapper, RexOmniVisualize

# Initialize model
model = RexOmniWrapper(
    model_path="IDEA-Research/Rex-Omni",
    backend="transformers"  # or "vllm"
)

# Load image
image = Image.open("your_image.jpg")

# Object Detection
results = model.inference(
    images=image,
    task="detection",
    categories=["person", "car", "dog"]
)

result = results[0]

# 4) Visualize
vis = RexOmniVisualize(
    image=image,
    predictions=result["extracted_predictions"],
    font_size=20,
    draw_width=5,
    show_labels=True,
)
vis.save("visualize.jpg")
2. Quick Start: Using Rex-Omni for Detectionpythonvllm
from PIL import Image
from rex_omni import RexOmniWrapper, RexOmniVisualize

# Initialize model
model = RexOmniWrapper(
    model_path="IDEA-Research/Rex-Omni",
    backend="transformers"  # or "vllm"
)

# Load image
image = Image.open("your_image.jpg")

# Object Detection
results = model.inference(
    images=image,
    task="detection",
    categories=["person", "car", "dog"]
)

result = results[0]

# 4) Visualize
vis = RexOmniVisualize(
    image=image,
    predictions=result["extracted_predictions"],
    font_size=20,
    draw_width=5,
    show_labels=True,
)
vis.save("visualize.jpg")
2. Quick Start: Using Rex-Omni for Detectionpythonvllm
from PIL import Image
from rex_omni import RexOmniWrapper, RexOmniVisualize

# Initialize model
model = RexOmniWrapper(
    model_path="IDEA-Research/Rex-Omni",
    backend="transformers"  # or "vllm"
)

# Load image
image = Image.open("your_image.jpg")

# Object Detection
results = model.inference(
    images=image,
    task="detection",
    categories=["person", "car", "dog"]
)

result = results[0]

# 4) Visualize
vis = RexOmniVisualize(
    image=image,
    predictions=result["extracted_predictions"],
    font_size=20,
    draw_width=5,
    show_labels=True,
)
vis.save("visualize.jpg")
2. Quick Start: Using Rex-Omni for Detectionpythonvllm
from PIL import Image
from rex_omni import RexOmniWrapper, RexOmniVisualize

# Initialize model
model = RexOmniWrapper(
    model_path="IDEA-Research/Rex-Omni",
    backend="transformers"  # or "vllm"
)

# Load image
image = Image.open("your_image.jpg")

# Object Detection
results = model.inference(
    images=image,
    task="detection",
    categories=["person", "car", "dog"]
)

result = results[0]

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

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