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AI Evaluation & Observability

Quiver

Quiver is an open-source developer tool that launches a local web dashboard to inspect Keras convolutional neural network layer activations and top predictions for input images.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Quiver — OpenViST project profile in AI Evaluation & Observability.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Quiver when you have a Keras CNN and want a local visual view of its layer activations and predictions on images.

Who it is for

  • Developers working with Keras convolutional neural networks.
  • Model builders who want to compare layer responses across input images.
  • Practitioners checking how input normalization relates to model predictions.

Documented uses

  • View layer activations for images passed to a Keras model.
  • Compare the model’s visual responses across a folder of images.
  • Inspect the top predictions alongside layer visualizations.
  • Set class labels, prediction count, temporary-file location, server port, or input mean and standard deviation.
Before you choose

Requirements & limits

The repository lists an MIT license and documents local installation and use; the primary workflow launches a dashboard on localhost.

  • Quiver’s documented model workflow is based on Keras.
  • It accepts an input-image folder and can display specified output class names.
  • The default number of top predictions is five; the README also documents a configurable top option.
  • The dashboard is served locally, with port 5000 documented as the default.
  • The launch options include custom mean and standard-deviation values for input data.

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

Launch Quiver for a Keras model

Start the local visualization dashboard for a Keras model and inspect activations for images in an input folder.

Before you start

  • A Keras model.
  • Quiver installed with pip.
  • An image folder if you want to explore a supplied set of images.

Steps

  1. Install Quiver with the command below.

    Documented command
    pip install quiver_engine

  2. Import server from quiver_engine in the Python environment containing the model.

  3. Call server.launch(model); to provide images and labels, pass input_folder and classes as launch options.

  4. Open the dashboard served at localhost:5000 and explore layer activations across the input images.

Expected outcome

A local dashboard for viewing model layer activations and top predictions.

Guide limitations

  • The README documents a default port of 5000; another port can be set with the port option.
  • The dashboard is documented for Keras models and image inputs.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
16 October 2026
Original website title
keplr-io/quiver · Official homepage title · retained in its source language
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