Transitioning From PyTorch to Burn

1. burn

   1 11 9,411 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"] }

2. SaaSHub

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   SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

   

3. resnet-burn

   2 1 25 4.5 Rust

   ResNet implementation with Burn

   

   The code used in this tutorial is available on GitHub.

4. vision

   3 23 16,474 9.2 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.

5. image

   4 37 5,096 9.3 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.

6. models

   5 1 203 8.0 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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