Crossvit Pytorch: Configure CrossViT image classification

Understand the project

Practical overview

Use the repository when you need a compact PyTorch example of configuring CrossViT and obtaining class outputs from an image tensor.

PyTorch developers exploring vision-transformer image classification.
Computer-vision practitioners adapting a classifier example to a specified image size, channel count and class count.
Researchers seeking an implementation to inspect alongside the CrossViT research work.

Configure a CrossViT model for image dimensions, channels and a chosen number of classes.
Pass an image tensor through the model and inspect class-output dimensions.
Study an unofficial PyTorch implementation of a multi-scale, cross-attention vision transformer.

Before you choose

The repository is public and lists an MIT license.

The README's demonstrated input is a tensor shaped [1, 3, 224, 224]; configure model parameters to match your intended input.
The example sets num_classes explicitly and documents outputs shaped [B, num_classes].
The repository describes itself as unofficial and acknowledges use of base ViT code from lucidrains’ vit-pytorch.
The README focuses on model invocation rather than data preparation, training or evaluation.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Configure the documented example model and obtain its class-output tensor.

Import torch and CrossViT from crossvit.
Create an input tensor, as in the example, with shape [1, 3, 224, 224].
Instantiate CrossViT with image_size=224, channels=3, and the desired num_classes (the example uses 100).
Call the model with the tensor and inspect the output, documented as shape [B, num_classes].

A tensor of class outputs for the input batch.

The README demonstrates a constructed tensor, not an image-loading, training, or evaluation workflow.
The example does not document installation instructions or model-weight loading.

Evidence and transparency

Source record details

Source availability: Active
Sources checked: 18 October 2026
Original website title: rishikksh20/CrossViT-pytorch · Official homepage title · retained in its source language