deep-implicit-attention
Implementation of deep implicit attention in PyTorch (by mcbal)
vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch (by lucidrains)
| deep-implicit-attention | vit-pytorch | |
|---|---|---|
| 1 | 11 | |
| 63 | 20,969 | |
| - | - | |
| 0.0 | 7.8 | |
| over 3 years ago | 14 days ago | |
| Python | Python | |
| MIT License | MIT License |
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
deep-implicit-attention
Posts with mentions or reviews of deep-implicit-attention.
We have used some of these posts to build our list of alternatives
and similar projects.
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[P] Deep Implicit Attention: A Mean-Field Theory Perspective on Attention Mechanisms
Code: https://github.com/mcbal/deep-implicit-attention
vit-pytorch
Posts with mentions or reviews of vit-pytorch.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-06-15.
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Is it easier to go from Pytorch to TF and Keras than the other way around?
I also need to learn Pyspark so right now I am going to download the Fashion Mnist dataset, use Pyspark to downsize each image and put the into separate folders according to their labels (just to show employers I can do some basic ETL with Pyspark, not sure how I am going to load for training in Pytorch yet though). Then I am going to write the simplest Le Net to try to categorize the fashion MNIST dataset (results will most likely be bad but it's okay). Next, try to learn transfer learning in Pytorch for both CNN or maybe skip ahead to ViT. Ideally at this point I want to study the Attention mechanism a bit more and try to implement Simple Vit which I saw here: https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/simple_vit.py
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What are the best resources online to learn attention and transformers?
For code implementation, check out this git repo. It contains fairly straightforward PyTorch implementations for various ViT papers with references.
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Training CNN/VIT on very small dataset
For ViT’s specifically, there’s been a good amount of research trying to extend ViT’s to work on small datasets without a large amount of pre-training (which comes with its own host of issues such as the best way to fine tune such a huge model). One paper which comes to mind is ViT’s for small datasets (https://arxiv.org/abs/2112.13492), which has an implementation in lucidrain’s repo here: https://github.com/lucidrains/vit-pytorch
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Transformers in RL
Here's a pytorch implementation of ViT https://github.com/lucidrains/vit-pytorch
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[P] Release the Vision Transformer Cookbook with Tensorflow ! (Thanks to @lucidrains)
looks great Junho! i've linked to it from https://github.com/lucidrains/vit-pytorch like you asked :)
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Will Transformers Take over Artificial Intelligence?
Sure thing. Also if you're getting into transformers I'd recommend lucidrains's GitHub[0] since it has a large collection of them with links to papers. It's nice that things are consolidated.
[0] https://github.com/lucidrains/vit-pytorch
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[D] Surprisingly Simple SOTA Self-Supervised Pretraining - Masked Autoencoders Are Scalable Vision Learners by Kaiming He et al. explained (5-minute summary by Casual GAN Papers)
nah, it is really simple. here is the code https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/mae.py
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[D] Training vision transformers on a specific dataset from scratch
lucid rains VI has all of what you may need in a clean API
- Can I train a tranaformer for image classification on Google colab??
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[R] Rotary Positional Embeddings - a new relative positional embedding for Transformers that significantly improves convergence (20-30%) and works for both regular and efficient attention
I've attempted it here https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/rvt.py but those who have tried it haven't seen knock out results as 1d. Perhaps the axial lengths are too small to see a benefit
What are some alternatives?
When comparing deep-implicit-attention and vit-pytorch you can also consider the following projects:
performer-pytorch - An implementation of Performer, a linear attention-based transformer, in Pytorch
reformer-pytorch - Reformer, the efficient Transformer, in Pytorch
TimeSformer-pytorch - Implementation of TimeSformer from Facebook AI, a pure attention-based solution for video classification
MLP-Mixer-pytorch - Unofficial implementation of MLP-Mixer: An all-MLP Architecture for Vision
soundstorm-pytorch - Implementation of SoundStorm, Efficient Parallel Audio Generation from Google Deepmind, in Pytorch
convolution-vision-transformers - PyTorch Implementation of CvT: Introducing Convolutions to Vision Transformers
spin-model-transformers - Physics-inspired transformer modules based on mean-field dynamics of vector-spin models in JAX
afem - Implementation of approximate free-energy minimization in PyTorch
DALLE-pytorch - Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch
x-transformers - A concise but complete full-attention transformer with a set of promising experimental features from various papers
efficient-attention - An implementation of the efficient attention module.
deep-implicit-attention vs performer-pytorch
vit-pytorch vs reformer-pytorch
deep-implicit-attention vs TimeSformer-pytorch
vit-pytorch vs MLP-Mixer-pytorch
deep-implicit-attention vs soundstorm-pytorch
vit-pytorch vs convolution-vision-transformers
deep-implicit-attention vs spin-model-transformers
vit-pytorch vs performer-pytorch
deep-implicit-attention vs afem
vit-pytorch vs DALLE-pytorch
deep-implicit-attention vs x-transformers
vit-pytorch vs efficient-attention