vision_transformer_tf
maxvit
vision_transformer_tf | maxvit | |
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4 | 1 | |
24 | 421 | |
- | 1.9% | |
10.0 | 0.0 | |
over 1 year ago | 11 months ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | Apache License 2.0 |
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vision_transformer_tf
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Implemented Vision Transformers from scratch using TensorFlow 2. x 🚀, Finetuning and Converting to TF-Lite ✅
Hi r/learnmachinelearning, I am done implementing the paper AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE, popularly known as the Vision Transformer paper. Using my implementation any vision transformer model can be finetuned pretty easily with any custom dataset, Converting weights to TensorFlow Lite is also supported. My codebase is also very straightforward to understand and debug. One can learn how the vision transformer works internally by debugging the whole pipeline. Link to the GitHub Project: https://github.com/TheTensorDude/vision_transformer_tf
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[P] Finetune any Vision Transformer architecture on your custom data 🚀, Convert to TensorFlow Lite ✅
The GitHub link to the project can be found here.
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[P] Implemented Vision Transformers 🚀 from scratch using TensorFlow 2.x
My implementation: GitHub Link
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Implemented Vision Transformers 🚀 from scratch using TensorFlow 2.x
My implementation: https://github.com/TheTensorDude/vision_transformer_tf
maxvit
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GOOGLE new computer vision multi-axis approach improves high level tasks, such as object detection, as well as motion deblurring, denoising, deraining
Today we present a new multi-axis approach that is simple and effective, improves on the original ViT and MLP models, can better adapt to high-resolution, dense prediction tasks, and can naturally adapt to different input sizes with high flexibility and low complexity. Based on this approach, we have built two backbone models for high-level and low-level vision tasks. We describe the first in “MaxViT: Multi-Axis Vision Transformer”, to be presented in ECCV 2022, and show it significantly improves the state of the art for high-level tasks, such as image classification, object detection, segmentation, quality assessment, and generation. The second, presented in “MAXIM: Multi-Axis MLP for Image Processing” at CVPR 2022, is based on a UNet-like architecture and achieves competitive performance on low-level imaging tasks including denoising, deblurring, dehazing, deraining, and low-light enhancement. To facilitate further research on efficient Transformer and MLP models, we have open-sourced the code and models for both MaxViT and MAXIM.
What are some alternatives?
coral-pi-rest-server - Perform inferencing of tensorflow-lite models on an RPi with acceleration from Coral USB stick
maxim - [CVPR 2022 Oral] Official repository for "MAXIM: Multi-Axis MLP for Image Processing". SOTA for denoising, deblurring, deraining, dehazing, and enhancement.
saliency - Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).
Azure-Computer-Vision-in-a-day-workshop - Azure Computer Vision 4 (March 2023 - Florence) workshop in a day
TFLiteClassification - TensorFlow Lite Image Classification Python Implementation
vision-transformer-from-scratch - A Simplified PyTorch Implementation of Vision Transformer (ViT)
gpt-mini - Yet another minimalistic Tensorflow (re-)re-implementation of Karpathy's Pytorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer).
astrophotography_stack_align - Align sequence of star field / astro images taken with a stationary camera (stationary relative to all those stars light years away).
optc-box-exporter - Export your One Piece Treasure Cruise Box with just using Screenshots
liga-pytorch - Let Data Dance with PyTorch Models