Batch - non interactive command line application
Summary
Object Detection in Images via Batch + Docker

App ID: osp.tutorial.apps.tutorialDemoObjDetectionExecutable | Version 0.1.0

Status: Enabled | App restrictions: Public

Owner: ben

Description

Object Detection

What it is

A ready-to-run container that finds everyday objects in a photograph. You give it an image; it gives you back the same image with boxes drawn around what it found, plus a machine-readable list of those objects, how confident it was about each one, and where in the picture they are.

It uses a general-purpose model trained to recognize eighty common categories — people, vehicles, animals, furniture, food, everyday household items. It cannot find things outside that list, so a picture with none of them in it correctly comes back empty.

This is the batch version: it runs once, does its work, and exits. There is nothing to click and nothing to watch. That makes it the right choice when you have a folder of images rather than a single interesting one.

Use cases

  • - Running detection across a large image set on a cluster, one job per batch.
  • - A step inside a larger pipeline, where the object list feeds whatever comes next.
  • - Teaching what a containerised analysis job looks like end to end, without the distraction of a user interface.
  • - Producing a consistent, reproducible baseline that other tools can be compared against — the model and its version are fixed inside the container, so the same image gives the same answer next year.
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What’s included

  • - The detection program itself.
  • - The trained model, built into the container. Nothing is downloaded when you run it, which means it works on machines with no internet access at all.
  • - Everything the program depends on, at fixed versions.

For each image you get an annotated picture and a structured results file.

Where it runs

  • On the AWS system, through Docker.
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Getting started

Choose your image in the app submission form. Results are written alongside the input, so a whole folder can be worked through the same way.

Reference

The Docker image is created from files hosted in https://github.com/OneSciencePlace/app-object-detection

That repository’s README has the exact commands for both Docker and Apptainer, along with the handful of options — most usefully the confidence threshold, which controls how sure the model has to be before it reports something.

If you would rather explore interactively before committing to a batch run, two companion apps do exactly that: one presents the same analysis as a notebook, the other as a desktop with a file browser.