solo-learn
dino
| solo-learn | dino | |
|---|---|---|
| 3 | 7 | |
| 1,358 | 5,881 | |
| - | 1.9% | |
| 5.9 | 1.0 | |
| 5 days ago | about 1 month ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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solo-learn
- [D]Use SimCLR as pretraining for image segmentation
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[D] State-of-the-Art for Self-Supervised (Pre-)Training of CNN architectures (e.g. ResNet)?
Hello, I lost a bit of touch to the current SOTA of self-supervised pretraining of CNNs, in particular ResNet. I found this repository https://github.com/vturrisi/solo-learn that has many methods implemented but I'm not really sure where to start. My goal is to pretrain a ResNet backbone on a decently large amount of image data that comes from a certain domain and after that fine-tune it for different downstream tasks (classification, segmentation, object detection) on a subset of the data I have labels for.
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[P] Solo-learn 1.0.3: new methods, support for transformer architectures, better evaluation, improved docs, and additional results.
Hi Reddit, the solo-learn team is back again with interesting news about its SSL library.
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?
lightning-transformers - Flexible components pairing 🤗 Transformers with :zap: Pytorch Lightning
simsiam-cifar10 - Code to train the SimSiam model on cifar10 using PyTorch
contrastive-reconstruction - Tensorflow-keras implementation for Contrastive Reconstruction (ConRec) : a self-supervised learning algorithm that obtains image representations by jointly optimizing a contrastive and a self-reconstruction loss.
Transformer-SSL - This is an official implementation for "Self-Supervised Learning with Swin Transformers".
Revisiting-Contrastive-SSL - Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [NeurIPS 2021]
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
Unsupervised-Semantic-Segmentation - Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals. [ICCV 2021]
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]
CEBRA - Learnable latent embeddings for joint behavioral and neural analysis - Official implementation of CEBRA
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)
vectorrvnn - Data Driven method for hierarchical grouping of paths in Vector Graphics.
lightly - A python library for self-supervised learning on images.