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Plip

Plip is a fine-tuned CLIP vision-language model for pathology that extracts features from pathology images and text descriptions, allowing their embeddings or similarity scores to be compared.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Plip — 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 Plip when a pathology research or development workflow needs image–text features or similarity scoring and you can provide the model inputs and build the surrounding application yourself.

Who it is for

  • Pathology AI researchers exploring image–text representation and similarity.
  • Developers building text-guided retrieval or candidate-label comparison prototypes for pathology images.
  • Teams organizing pathology image collections around text descriptions or embedding-based comparisons.

Documented uses

  • Encode pathology images and text descriptions as features for comparison.
  • Compare a pathology image with candidate text labels using image–text similarity scores.
  • Build a prototype for text-guided pathology image retrieval or embedding-based collection organization.
Before you choose

Requirements & limits

  • The repository documents both a Python encoding interface and a Hugging Face Transformers example.
  • The Hugging Face example processes an image alongside candidate labels and exposes image–text similarity scores.
  • The repository metadata lists no license, so verify reuse terms before adopting the model.
  • The repository links to an official demo and a Hugging Face model page, but the documented code examples describe model use rather than a complete application.

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

Score a pathology image against candidate text labels

Use the documented Hugging Face Transformers example to obtain image–text similarity scores for one image and a set of text labels.

Before you start

  • Python environment with the Transformers and Pillow packages available.
  • Access to the Hugging Face model identifier vinid/plip.
  • An image file to process and candidate text labels.

Steps

  1. Load CLIPModel and CLIPProcessor from the vinid/plip model identifier.

  2. Open an image with PIL and pass it with candidate text labels to the processor, requesting tensor outputs and padding.

  3. Pass the processed inputs to the model and read outputs.logits_per_image for the image–text similarity scores.

  4. Apply softmax to the scores, as shown in the repository example, to produce normalized values across the candidate labels.

Expected outcome

A score for each candidate label describing its similarity to the image, with the example also showing softmax-normalized values.

Guide limitations

  • The example covers one image and candidate labels; it does not document a complete collection search or a clinical interpretation workflow.
  • The repository does not state software licensing terms.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
PathologyFoundation/plip · Official homepage title · retained in its source language
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