OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Models

CvT: Introducing Convolutions to Vision Transformers

CvT is a Python and PyTorch implementation of the Convolutional Vision Transformer architecture for computer-vision image classification.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

CvT: Introducing Convolutions to Vision Transformers — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use the repository when you need a PyTorch CvT implementation and want to examine its model call and output dimensions. The README example is a starting point for model use, not a complete image-classification pipeline.

Who it is for

  • Developers exploring or adapting a CvT image-classification model in PyTorch.
  • Computer-vision practitioners who need model code that returns class scores for image tensors.

Documented uses

  • Construct a CvT for a specified input size, channel count, and number of classes.
  • Pass a batch-shaped image tensor through the model and inspect its class-score output.
  • Count the model’s trainable parameters using the documented example.
Before you choose

Requirements & limits

The repository is public and licensed under MIT.

  • The demonstrated configuration takes three-channel 224 × 224 inputs and produces scores for 1,000 classes.
  • The usage example uses a tensor of ones and does not document image preprocessing or evaluation.
  • The repository identifies Microsoft’s CvT repository as the official implementation.

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

Construct a CvT and inspect its output shape

Follow the README example to pass a 224 × 224, three-channel tensor through a CvT model configured for 1,000 classes.

Before you start

  • The repository’s Python implementation and its PyTorch and NumPy imports.
  • An input tensor shaped [1, 3, 224, 224].

Steps

  1. Create an input tensor using the documented shape and values.

  2. Construct CvT with arguments 224, 3, and 1000.

  3. Pass the tensor to the model and inspect the output shape.

Expected outcome

The example reports an output shaped [batch size, number of classes], which is [1, 1000] for its shown input.

Guide limitations

  • The example uses a tensor of ones, not an image-processing pipeline.
Documentation 1 ↗Documentation 2 ↗
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
rishikksh20/convolution-vision-transformers · Official homepage title · retained in its source language
How we review sourcesSuggest a correction