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Clip

Clip is an MIT-licensed neural network trained on image-text pairs that compares an image with natural-language text candidates and predicts the most relevant matches without direct task-specific optimization.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Clip — 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 Clip when the labels or search concepts can be expressed as text and you need image-text matching without training a classifier directly for each task.

Who it is for

  • Developers building image search or tagging tools
  • Machine-learning practitioners evaluating zero-shot image classification
  • Teams needing image and text feature representations for downstream models

Documented uses

  • Match an image against natural-language labels
  • Rank text candidates for an image
  • Create zero-shot image classification workflows
  • Extract image or text features for downstream evaluation
Before you choose

Requirements & limits

The public repository is accessible and identifies an MIT license. The documented setup supports CUDA or CPU execution; no account or pricing requirement is specified.

  • Candidate labels are supplied as natural-language text, so result quality depends on how those prompts are written.
  • The repository returns cosine-similarity-based logits for corresponding image and text features.
  • The model can run on a CUDA device or CPU, with the device selected when loading the model.
  • A local checkpoint can be supplied, while model loading may otherwise download the requested model.
  • The repository documents PyTorch and TorchVision as core prerequisites.

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

Set up Clip for image-text matching

Install the repository and prepare a model, image and text candidates for similarity scoring.

Before you start

  • PyTorch 1.7.1 or later
  • torchvision
  • ftfy, regex and tqdm
  • A CUDA-capable machine with the appropriate CUDA package, or a CPU-only PyTorch installation
  • Python access to the public Clip repository

Steps

  1. Install PyTorch and torchvision, then install ftfy, regex and tqdm.

  2. Install the Clip repository as a Python package.

  3. Load a model such as ViT-B/32 and select CUDA when available or CPU otherwise.

  4. Open and preprocess an image, tokenize candidate text strings, and pass both inputs to the model.

  5. Read the returned image-text logits and apply softmax when probability-like scores are needed.

Expected outcome

A prepared Clip model produces similarity scores for the supplied image and candidate text strings.

Guide limitations

  • The repository may download the selected model when it is loaded unless a local checkpoint path is used.
  • The documented example reports exact output values that can vary by compute device.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
1 October 2026
Original website title
openai/CLIP · Official homepage title · retained in its source language
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