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Francesco Zuppichini
Francesco Zuppichini

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Published in Towards Data Science

·Pinned

Implementing SegFormer in PyTorch

A fast, efficient, and lightweight model for image segmentation — Hello There!! Today we’ll see how to implement SegFormer in PyTorch proposed in SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers. Code is here, an interactive version of this article can be downloaded from here. Let’s get started! The paper proposes a new transformer-based model to tackle image…

Deep Learning

6 min read

Implementing SegFormer in PyTorch
Implementing SegFormer in PyTorch

Published in Towards Data Science

·Pinned

Implementing ConvNext in PyTorch

A new convnet that beats transformers — Hello There!! Today we are going to implement the famous ConvNext in PyTorch proposed in A ConvNet for the 2020s . Code is here, an interactive version of this article can be downloaded from here. Let’s get started! The paper proposes a new convolution-based architecture that not only surpasses Transformer-based…

Deep Learning

5 min read

Implementing ConvNext in PyTorch
Implementing ConvNext in PyTorch

Published in Towards Data Science

·Pinned

Distilling Transformers: (DeiT) Data-efficient Image Transformers

transformers go brum brum Hi guys! Today we are going to implement Training data-efficient image transformers & distillation through attention a new method to perform knowledge distillation on Vision Transformers called DeiT. You will soon see how elegant and simple this new approach is. Code is here, an interactive version…

Deep Learning

4 min read

Distilling Transformers: (DeiT) Data-efficient Image Transformers
Distilling Transformers: (DeiT) Data-efficient Image Transformers

Published in Towards Data Science

·Pinned

Implementing Vision Transformer (ViT) in PyTorch

Hi guys, happy new year! Today we are going to implement the famous Vi(sion) T(ransformer) proposed in AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE. Code is here, an interactive version of this article can be downloaded from here. ViT is available on my new computer…

Artificial Intelligence

6 min read

Implementing VisualTtransformer in PyTorch
Implementing VisualTtransformer in PyTorch

Apr 14

Loading Huge PyTorch Models with Linear Memory Consumption

Hello There! Code is here Today we will see how to load a Pytorch model with linear memory consumption. Loading a model takes 2x memory space. Let’s see why: First, we need a model: Upon creation, the model takes 1x memory, where x is its size At some…

Machine Learning

3 min read

Loading Huge PyTorch Models with Linear Memory Consumption
Loading Huge PyTorch Models with Linear Memory Consumption

Published in Towards Data Science

·Feb 7

Implementing Stochastic Depth/Drop Path In PyTorch

DropPath is available in glasses my computer vision library — Code is here an interactive version of this article can be downloaded from here. Introduction Today we are going to implement Stochastic Depth also known as Drop Path in PyTorch! Stochastic Depth introduced by Gao Huang et al is a technique to “deactivate” some layers during training. We’ll stick with DropPath. …

Computer Vision

5 min read

Implementing Stochastic Depth/Drop Path In PyTorch
Implementing Stochastic Depth/Drop Path In PyTorch

Published in Towards Data Science

·Oct 27, 2021

Residual, BottleNeck, Inverted Residual, Linear BottleNeck, MBConv Explained

What the hell are those + implementation in PyTorch — An interactive version is available here All these blocks have been implemented in my library glasses Keeping track of names in modern Deep Learning is hard. Today we’ll see different blocks used in modern CNN architecture such as ResNet, MobileNet, EfficientNet, and their implementation in PyTorch! Before we do anything…

Programming

5 min read

Residual, BottleNeck, Inverted Residual, Linear BottleNeck, MBConv Explained
Residual, BottleNeck, Inverted Residual, Linear BottleNeck, MBConv Explained

Published in Towards Data Science

·Sep 5, 2021

A better Dropout! Implementing DropBlock in PyTorch

An interactive version of this article can be found here DropBlock is available on glasses in my computer vision library! Introduction Today we are going to implement DropBlock in PyTorch! DropBlock introduced by Ghiasi et al is a regularization technique specifical crafter for images that empirically works better than Dropout. …

Deep Learning

5 min read

A better Dropout! Implementing DropBlock in PyTorch
A better Dropout! Implementing DropBlock in PyTorch

Published in Towards Data Science

·Oct 13, 2020

Face Unlock with 2D Data

A deep learning approach — All the code can be found here. An interactive version of this article can be downloaded from here Today we are going to use deep learning to create a face unlock algorithm. To complete our puzzle, we need three main pieces. a find faces algorithm a way to embed the…

Python

5 min read

Face Unlock with 2D Data
Face Unlock with 2D Data

Published in Towards Data Science

·Jun 11, 2020

Deploy models in PyTorch 🚀

torchserve to the rescue! — All the code used in this article is here Recently, PyTorch has introduced its new production framework to properly serve models, called torchserve.So, without further due, let’s present today’s roadmap: Installation with Docker Export your model Define a handler Serve our model To showcase torchserve, we will serve a fully…

Pytorch

6 min read

Deploy models and create custom handlers in Torchserve 🚀
Deploy models and create custom handlers in Torchserve 🚀
Francesco Zuppichini

Francesco Zuppichini

Computer Vision Engineer @ 🤗

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