Transitioning From PyTorch to Burn

• burn

  8 7,020 9.8 Rust

  Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.

  

[package] name = "resnet_burn" version = "0.1.0" edition = "2021" [dependencies] burn = { git = "https://github.com/tracel-ai/burn.git", rev = "75cb5b6d5633c1c6092cf5046419da75e7f74b11", features = ["ndarray"] } burn-import = { git = "https://github.com/tracel-ai/burn.git", rev = "75cb5b6d5633c1c6092cf5046419da75e7f74b11" } image = { version = "0.24.7", features = ["png", "jpeg"] }

• resnet-burn

  1 22 5.8 Rust

  ResNet implementation with Burn

The code used in this tutorial is available on GitHub.

• WorkOS

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• vision

  19 15,423 9.5 Python

  Datasets, Transforms and Models specific to Computer Vision

  

Let's start by defining the ResNet module according to the Residual Network architecture, as replicated[1] by the torchvision implementation of the model we will import. Detailed architecture variants with a depth of 18, 34, 50, 101 and 152 layers can be found in the table below.

• image

  37 4,505 9.1 Rust

  Encoding and decoding images in Rust (by image-rs)

  

With the help of the image crate, loading an image from disk is fairly straightforward.

• models

  1 99 7.7 Rust

  Models and examples built with Burn (by tracel-ai)

  

If you want to use ResNet in your application, take a look at the official Burn implementation available on GitHub! It closely follows this tutorial's implementation but further extends it to provide an easy interface to load the pre-trained weights for the whole ResNet family of models.

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