Model Card for CLIP ViT-H/14 frozen xlm roberta large - LAION-5B
Table of Contents
- Model Details
- Uses
- Training Details
- Evaluation
- Acknowledgements
- Citation
- How To Get Started With the Model
Model Details
Model Description
A CLIP ViT-H/14 frozen xlm roberta large model trained with the LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip).
Model training done by Romain Beaumont on the stability.ai cluster.
Uses
Direct Use
Zero-shot image classification, image and text retrieval, among others.
Downstream Use
Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others.
Training Details
Training Data
This model was trained with the full LAION-5B (https://laion.ai/blog/laion-5b/).
Training Procedure
Training with batch size 90k for 13B sample of laion5B, see https://wandb.ai/rom1504/open-clip/reports/xlm-roberta-large-unfrozen-vit-h-14-frozen--VmlldzoyOTc3ODY3
Model is H/14 on visual side, xlm roberta large initialized with pretrained weights on text side.
The H/14 was initialized from https://huggingface.co./laion/CLIP-ViT-H-14-laion2B-s32B-b79K and kept frozen during training.
Evaluation
Evaluation done with code in the LAION CLIP Benchmark suite.
Testing Data, Factors & Metrics
Testing Data
The testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval.
Results
The model achieves imagenet 1k 77.0% (vs 78% for the english H/14)
On zero shot classification on imagenet with translated prompts this model reaches:
- 56% in italian (vs 21% for https://github.com/clip-italian/clip-italian)
- 53% in japanese (vs 54.6% for https://github.com/rinnakk/japanese-clip)
- 55.7% in chinese (to be compared with https://github.com/OFA-Sys/Chinese-CLIP)
This model reaches strong results in both english and other languages.
Acknowledgements
Acknowledging stability.ai for the compute used to train this model.
Citation
BibTeX:
In addition to forthcoming LAION-5B (https://laion.ai/blog/laion-5b/) paper, please cite:
OpenAI CLIP paper
@inproceedings{Radford2021LearningTV,
title={Learning Transferable Visual Models From Natural Language Supervision},
author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
booktitle={ICML},
year={2021}
}
OpenCLIP software
@software{ilharco_gabriel_2021_5143773,
author = {Ilharco, Gabriel and
Wortsman, Mitchell and
Wightman, Ross and
Gordon, Cade and
Carlini, Nicholas and
Taori, Rohan and
Dave, Achal and
Shankar, Vaishaal and
Namkoong, Hongseok and
Miller, John and
Hajishirzi, Hannaneh and
Farhadi, Ali and
Schmidt, Ludwig},
title = {OpenCLIP},
month = jul,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5143773},
url = {https://doi.org/10.5281/zenodo.5143773}
}
How To Get Started With the Model
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