Explore the marketplace. Items labelled Example are fictional. Video reviewed means a human has reviewed the introduction, not the engineering qualifications. Sign in to contact a client about a published project. Project payments are not open.YOUR ENGINEER
In progress

Prototype a visual inspection workflow

Evaluate one surface-defect detection approach and give operators a simple way to review its mistakes.

Signalgarden Manufacturing

Singapore Remote

Posted

This is an example brief. Its company, dates, budget, assignment and progress illustrate a project; they do not represent available or commissioned work. Proposals cannot be submitted.

The brief

The work and the intended result

We have 1,200 labelled photographs of moulded covers from a single camera station. This feasibility package compares a simple image-feature baseline with one compact pretrained model, using a held-out production batch. It includes a local review screen for up to three defect categories and two review sessions. It does not include camera installation, production integration or a guaranteed detection rate. A recommendation against deployment is an acceptable result if the evaluation supports it.

Duration
4 weeks
Commitment
Approx. 70 hours total
Location
Remote

What you’ll deliver

  • A data audit covering class balance, duplicate images and batch separation
  • Reproducible baseline and model scripts with a fixed evaluation split
  • A local image-review interface showing predictions, labels and operator comments
  • An evaluation report with per-class errors, failure examples and a scoped next-step recommendation

Expertise needed

  • Practical experience evaluating image classifiers on small industrial datasets
  • Python implementation with a documented environment and repeatable commands
  • Ability to explain false positives, missed defects and uncertain labels to non-specialists
  • Python
  • Computer vision
  • Machine learning
  • API development

A running start

What’s already in place

  • 1,200 image files with batch identifiers and three-category annotations
  • A sample set of 30 disputed labels for joint review
  • A product owner and one operator for two one-hour feedback sessions

Project milestones

From brief to handover
  1. 01

    Audit and freeze the evaluation set

    Completed: reviewed the supplied labels and separated the held-out batch from training inputs.

  2. 02

    Compare models and inspect errors

    Current: evaluate the two approaches and connect the selected results to the operator review screen.

  3. 03

    Deliver a reproducible recommendation

    Confirm a clean setup reproduces the report, review ten representative errors and hand over the source.

Working together

A shared definition of done

Confirm the deliverables, review checks and response window together so everyone knows what a successful handover looks like.

Suggested acceptance criteria

  • Every agreed deliverable is included in the handover, in the agreed file formats.
  • Assumptions, limitations and open questions are documented alongside the work.
  • The project owner reviews the work against the brief and returns one consolidated feedback list before approval.

When the scope changes

Record the requested change, its reason and its effect on deliverables, budget and timing. Both parties agree the revised scope in writing before the additional work begins.