Models

What is ResNet 32?

A fast, simple convolutional neural network that gets the job done for many tasks, including classification.

About the model

Here is an overview of the

ResNet 32

model:

Date of Release
Model Type Classification
Architecture
Framework Used Fast.ai v2
Annotation Format
Stars on GitHub 2+

What is ResNet-32?

ResNet-32 is a convolutional neural network backbone that is based off alternative ResNet networks such as ResNet-34, ResNet-50, and ResNet-101. As its name implies, ResNet-32 is has 32 layers. It addresses the problem of vanishing gradient with the identity shortcut connection that skips one or more layers. The ResNet backbone can be ported into many applications including image classification as it is used here. This implementation of ResNet-32 is created with fastai, a low code deep learning framework.

ResNet-32 Architecture

ResNet-32's Architecture is largely inspired by the architecture of ResNet-34. Below, on the right-hand side, is Resnet34's architecture where the 34 layers and the residuals from one layer to another are visualized.

Restnet34 Architecture

Further Reading

ResNet-32 Original Repository: https://github.com/verrannt/Tutorials

Check out YOLOv8, defining a new state-of-the-art in computer vision

YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.

Learn about YOLOv8

Model Performance

Explore this model on Roboflow

No items found.

Deploy ResNet 32 to production

Roboflow offers a range of SDKs with which you can deploy your model to production.

No items found.

ResNet 32 Annotation Format

ResNet 32

uses the

uses the

annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.

Convert data between formats

Label data automatically with ResNet 32

You can automatically label a dataset using

ResNet 32

with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use

ResNet 32

to train a computer vision model.

No items found.

Deploy a computer vision model today

Join 100k developers curating high quality datasets and deploying better models with Roboflow.

Get started
MANAGING over 100 million images for companies of all sizes

Join over 250,000 developers managing computer vision data on Roboflow.

VentureBeatTechCrunchInteresting EngineeringInternational Business TimesU.S. News & World ReportYahoo Finance