The S4 repository presents structured state space sequence models for sequence modeling research.
Open source
24 alternativesfacebookresearch/ImageBind
ImageBind explores a shared embedding space that connects different kinds of data.
About facebookresearch/ImageBind
ImageBind provides a PyTorch implementation and pretrained models that map images, text, audio, depth, thermal, and IMU data into a joint embedding space. It includes code to extract and compare features across modalities for cross-modal retrieval, composing modalities with arithmetic, and cross-modal detection and generation.
Use requires PyTorch 2.0+ and other dependencies, with Python 3.10 shown in the setup example; Windows users may need to install soundfile for audio files. Code and model weights are released under the CC-BY-NC 4.0 license.
Key features
- Joint embedding for images, text, audio, depth, thermal, and IMU data
- Pretrained imagebind_huge model available as checkpoint download
- Extract and compare features across modalities with PyTorch
- Cross-modal retrieval across supported modalities
- Compose modalities using arithmetic
- Cross-modal detection and generation
Best for: Researchers and developers using PyTorch who need to compare image, text, audio, depth, thermal, and IMU data in one embedding space.
Good for
- PyTorch implementation with pretrained models included
- Single embedding space covers six data types
- Example code shows feature extraction and comparison
Watch out for
- Requires PyTorch 2.0+ and other third-party dependencies
- Windows users may need extra soundfile package for audio
- Code and model weights are under CC-BY-NC 4.0 license
facebookresearch/ImageBind at a glance
- Category
- AI & Machine Learning › Models & ML Research
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Platforms
- Windows
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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facebookresearch/ImageBind FAQ
What data types does ImageBind handle?
Images, text, audio, depth, thermal, and IMU data in a joint embedding space.
What is needed to install and run it?
PyTorch 2.0+ and other dependencies; the example uses Python 3.10 with conda and pip install, plus soundfile on Windows for audio files.
What license covers the code and weights?
CC-BY-NC 4.0 license; see LICENSE for additional details.
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