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VirTex: Learning Visual Representations from Textual Annotations

Karan Desai and Justin Johnson
University of Michigan


CVPR 2021 arxiv.org/abs/2006.06666

Model Zoo, Usage Instructions and API docs: kdexd.github.io/virtex

VirTex is a pretraining approach which uses semantically dense captions to learn visual representations. We train CNN + Transformers from scratch on COCO Captions, and transfer the CNN to downstream vision tasks including image classification, object detection, and instance segmentation. VirTex matches or outperforms models which use ImageNet for pretraining -- both supervised or unsupervised -- despite using up to 10x fewer images.

virtex-model

Get the pretrained ResNet-50 visual backbone from our best performing VirTex model in one line without any installation!

import torch

# That's it, this one line only requires PyTorch.
model = torch.hub.load("kdexd/virtex", "resnet50", pretrained=True)

Note (For returning users before January 2021):

The pretrained models in our model zoo have changed from v1.0 onwards. They are slightly better tuned than older models, and reproduce the results in our CVPR 2021 accepted paper (arXiv v2). Some training and evaluation hyperparams are changed since v0.9. Please refer CHANGELOG.md

Usage Instructions

  1. How to setup this codebase?
  2. VirTex Model Zoo
  3. How to train your VirTex model?
  4. How to evaluate on downstream tasks?

Full documentation is available at kdexd.github.io/virtex.

Citation

If you find this code useful, please consider citing:

@inproceedings{desai2021virtex,
    title={{VirTex: Learning Visual Representations from Textual Annotations}},
    author={Karan Desai and Justin Johnson},
    booktitle={CVPR},
    year={2021}
}

Acknowledgments

We thank Harsh Agrawal, Mohamed El Banani, Richard Higgins, Nilesh Kulkarni and Chris Rockwell for helpful discussions and feedback on the paper. We thank Ishan Misra for discussions regarding PIRL evaluation protocol; Saining Xie for discussions about replicating iNaturalist evaluation as MoCo; Ross Girshick and Yuxin Wu for help with Detectron2 model zoo; Georgia Gkioxari for suggesting the Instance Segmentation pretraining task ablation; and Stefan Lee for suggestions on figure aesthetics. We thank Jia Deng for access to extra GPUs during project development; and UMich ARC-TS team for support with GPU cluster management. Finally, we thank all the Starbucks outlets in Ann Arbor for many hours of free WiFi. This work was partially supported by the Toyota Research Institute (TRI). However, note that this article solely reflects the opinions and conclusions of its authors and not TRI or any other Toyota entity.