Transformer-SSL
dino
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| Transformer-SSL | dino | |
|---|---|---|
| 2 | 7 | |
| 589 | 5,854 | |
| 0.0% | 3.4% | |
| 0.0 | 1.0 | |
| almost 3 years ago | 19 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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Transformer-SSL
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[R] A new baseline and codebase for self-supervised learning (SSL) with ViT/Swin-Transformer (Microsoft Research)
Github: https://github.com/SwinTransformer/Transformer-SSL
- [P] A new codebase for self-supervised learning with vision Transformers that provides evaluation on down-stream tasks of object detection and semantic segmentation
dino
- Batch-wise processing or image-by-image processing? (DINO V1)
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[P] Image search with localization and open-vocabulary reranking.
I also implemented one based on the self attention maps from the DINO trained ViT’s. This worked pretty well when the attention maps were combined with some traditional computer vision to get bounding boxes. It seemed an ok compromise between domain specialization and location specificity. I did not try any saliency or gradient based methods as i was not sure on generalization and speed respectively. I know LAVIS has an implementation of grad cam and it seems to work well in the plug'n'play vqa.
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Unsupervised semantic segmentation
You will probably need an unwieldy amount of data and compute to reproduce it, so your best option would be to use the pretrained models available on github.
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[D] Why Transformers are taking over the Compute Vision world: Self-Supervised Vision Transformers with DINO explained in 7 minutes!
[Full Explanation Post] [Arxiv] [Project Page]
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A major part of real-world AI has to be solved to make unsupervised, generalized full self-driving work, as the entire road system is designed for biological neural nets with optical imagers
Except he is actually talking about the new DINO model created by facebook that was released on friday. Which is a new approach to image transformers for unsupervised segmentation. Here's its github.
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[D] Paper Explained - DINO: Emerging Properties in Self-Supervised Vision Transformers (Full Video Analysis)
Code: https://github.com/facebookresearch/dino
- [R] DINO and PAWS: Advancing the state of the art in computer vision with self-supervised Transformers
What are some alternatives?
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
simsiam-cifar10 - Code to train the SimSiam model on cifar10 using PyTorch
byol-pytorch - Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch
lightly - A python library for self-supervised learning on images.
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
Unsupervised-Classification - SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]
unsupervised-depth-completion-visual-inertial-odometry - Tensorflow and PyTorch implementation of Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)
solo-learn - solo-learn: a library of self-supervised methods for visual representation learning powered by Pytorch Lightning
suncet - Code to reproduce the results in the FAIR research papers "Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples" https://arxiv.org/abs/2104.13963 and "Supervision Accelerates Pre-training in Contrastive Semi-Supervised Learning of Visual Representations" https://arxiv.org/abs/2006.10803
self-supervised - Whitening for Self-Supervised Representation Learning | Official repository