performer-pytorch
scenic
| performer-pytorch | scenic | |
|---|---|---|
| 2 | 5 | |
| 1,055 | 3,028 | |
| - | 3.9% | |
| 1.8 | 8.6 | |
| over 2 years ago | 2 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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.
performer-pytorch
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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
Performer is the best linear attention variant, but linear attention is just one type of efficient attention solution. I have rotary embeddings already in the repo https://github.com/lucidrains/performer-pytorch and you can witness this phenomenon yourself by toggling it on / off
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Why has Google's Performer model not replaced traditional softmax attention?
Here's an PyTorch implementation if you want to play around with it: lucidrains/performer-pytorch: An implementation of Performer, a linear attention-based transformer, in Pytorch (github.com)
scenic
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Vid2Seq: A pretrained visual language model for describing multi-event videos
Anyone figured out how to run this against a video?
https://github.com/google-research/scenic/tree/main/scenic/p... has an example showing how to "train Vid2Seq on YouCook2" using "python -m scenic.projects.vid2seq.main", but I couldn't see the recipe for using it against a video to return a description.
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[D] SE for machine learning reaserch
There are a few libraries/frameworks that one can use and allow to reuse the same code for datasets, logging, training loop etc.... . E.g. Lightning or Scenic. Maybe you can use one of these or at least get some inspiration for your own code.
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Google Research Proposes an Artificial Intelligence (AI) Model to Utilize Vision Transformers on Videos
Quick Read: https://www.marktechpost.com/2022/11/25/google-research-proposes-an-artificial-intelligence-ai-model-to-utilize-vision-transformers-on-videos/ Paper: https://openaccess.thecvf.com/content/ICCV2021/papers/Arnab\_ViViT\_A\_Video\_Vision\_Transformer\_ICCV\_2021\_paper.pdf Github link: https://github.com/google-research/scenic/tree/main/scenic/projects/vivit
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Google Research Introduces ‘SCENIC’: An Open-Source JAX Library For Computer Vision Research
GitHub: https://github.com/google-research/scenic
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[R] Google Open-Sources SCENIC: A JAX Library for Rapid Computer Vision Model Prototyping and Cutting-Edge Research
The SCENIC code, etc., has been open-sourced on the project’s GitHub. The paper SCENIC: A JAX Library for Computer Vision Research and Beyond is on arXiv.
What are some alternatives?
long-range-arena - Long Range Arena for Benchmarking Efficient Transformers
jax-resnet - Implementations and checkpoints for ResNet, Wide ResNet, ResNeXt, ResNet-D, and ResNeSt in JAX (Flax).
Perceiver - Implementation of Perceiver, General Perception with Iterative Attention in TensorFlow
manga-ocr - Optical character recognition for Japanese text, with the main focus being Japanese manga
memory-efficient-attention-pytorch - Implementation of a memory efficient multi-head attention as proposed in the paper, "Self-attention Does Not Need O(n²) Memory"
EasyCV - An all-in-one toolkit for computer vision
reformer-pytorch - Reformer, the efficient Transformer, in Pytorch
elegy - A High Level API for Deep Learning in JAX
vit-pytorch - Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
asreview - Active learning for systematic reviews
deep-implicit-attention - Implementation of deep implicit attention in PyTorch
LFattNet - Attention-based View Selection Networks for Light-field Disparity Estimation