Try the Model

Use the widget below to experiment with ResNet 32. You can detect COCO classes such as people, vehicles, animals, household items.

Overview

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.

ResNet 32 License

ResNet 32

is licensed under a

license.

Performance

Deploy a ResNet 32 API

You can use Roboflow Inference to deploy a

ResNet 32

API on your hardware. You can deploy the model on CPU (i.e. Raspberry Pi, AI PCs) and GPU devices (i.e. NVIDIA Jetson, NVIDIA T4).

Below are instructions on how to deploy your own model API.

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.

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