Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/prabin-acharya/runpod-imagebind-flask-api
https://github.com/prabin-acharya/runpod-imagebind-flask-api
Last synced: 5 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/prabin-acharya/runpod-imagebind-flask-api
- Owner: prabin-acharya
- License: other
- Created: 2024-05-05T19:46:08.000Z (9 months ago)
- Default Branch: master
- Last Pushed: 2024-05-05T19:57:46.000Z (9 months ago)
- Last Synced: 2024-05-06T20:46:34.224Z (9 months ago)
- Language: Python
- Size: 1.29 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# ImageBind: One Embedding Space To Bind Them All
**[FAIR, Meta AI](https://ai.facebook.com/research/)**
Rohit Girdhar*,
Alaaeldin El-Nouby*,
Zhuang Liu,
Mannat Singh,
Kalyan Vasudev Alwala,
Armand Joulin,
Ishan Misra*
To appear at CVPR 2023 (*Highlighted paper*)
[[`Paper`](https://facebookresearch.github.io/ImageBind/paper)] [[`Blog`](https://ai.facebook.com/blog/imagebind-six-modalities-binding-ai/)] [[`Demo`](https://imagebind.metademolab.com/)] [[`Supplementary Video`](https://dl.fbaipublicfiles.com/imagebind/imagebind_video.mp4)] [[`BibTex`](#citing-imagebind)]
PyTorch implementation and pretrained models for ImageBind. For details, see the paper: **[ImageBind: One Embedding Space To Bind Them All](https://facebookresearch.github.io/ImageBind/paper)**.
ImageBind learns a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. It enables novel emergent applications ‘out-of-the-box’ including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation.
## ImageBind model
Emergent zero-shot classification performance.
Model
IN1k
K400
NYU-D
ESC
LLVIP
Ego4D
download
imagebind_huge
77.7
50.0
54.0
66.9
63.4
25.0
checkpoint
## Usage
Install pytorch 1.13+ and other 3rd party dependencies.
```shell
conda create --name imagebind python=3.8 -y
conda activate imagebind
pip install .
```
For windows users, you might need to install `soundfile` for reading/writing audio files. (Thanks @congyue1977)
```
pip install soundfile
```
Extract and compare features across modalities (e.g. Image, Text and Audio).
```python
from imagebind import data
import torch
from imagebind.models import imagebind_model
from imagebind.models.imagebind_model import ModalityType
text_list=["A dog.", "A car", "A bird"]
image_paths=[".assets/dog_image.jpg", ".assets/car_image.jpg", ".assets/bird_image.jpg"]
audio_paths=[".assets/dog_audio.wav", ".assets/car_audio.wav", ".assets/bird_audio.wav"]
device = "cuda:0" if torch.cuda.is_available() else "cpu"
# Instantiate model
model = imagebind_model.imagebind_huge(pretrained=True)
model.eval()
model.to(device)
# Load data
inputs = {
ModalityType.TEXT: data.load_and_transform_text(text_list, device),
ModalityType.VISION: data.load_and_transform_vision_data(image_paths, device),
ModalityType.AUDIO: data.load_and_transform_audio_data(audio_paths, device),
}
with torch.no_grad():
embeddings = model(inputs)
print(
"Vision x Text: ",
torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T, dim=-1),
)
print(
"Audio x Text: ",
torch.softmax(embeddings[ModalityType.AUDIO] @ embeddings[ModalityType.TEXT].T, dim=-1),
)
print(
"Vision x Audio: ",
torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.AUDIO].T, dim=-1),
)
# Expected output:
#
# Vision x Text:
# tensor([[9.9761e-01, 2.3694e-03, 1.8612e-05],
# [3.3836e-05, 9.9994e-01, 2.4118e-05],
# [4.7997e-05, 1.3496e-02, 9.8646e-01]])
#
# Audio x Text:
# tensor([[1., 0., 0.],
# [0., 1., 0.],
# [0., 0., 1.]])
#
# Vision x Audio:
# tensor([[0.8070, 0.1088, 0.0842],
# [0.1036, 0.7884, 0.1079],
# [0.0018, 0.0022, 0.9960]])
```
## Model card
Please see the [model card](model_card.md) for details.
## License
ImageBind code and model weights are released under the CC-BY-NC 4.0 license. See [LICENSE](LICENSE) for additional details.
## Contributing
See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md).
## Citing ImageBind
If you find this repository useful, please consider giving a star :star: and citation
```
@inproceedings{girdhar2023imagebind,
title={ImageBind: One Embedding Space To Bind Them All},
author={Girdhar, Rohit and El-Nouby, Alaaeldin and Liu, Zhuang
and Singh, Mannat and Alwala, Kalyan Vasudev and Joulin, Armand and Misra, Ishan},
booktitle={CVPR},
year={2023}
}
```