10.6.1 Project Roadmap: Build a Vision Evidence Pack

A computer vision project is not “I used a model.” It is a loop of data, annotation, model output, metrics, failure cases, and presentation.

See the Project Loop First

Start with classification if you need the fastest complete loop. Move to detection for boxes, segmentation for masks, and OCR/video/3D for specialized scenarios.

Run a Project Readiness Check

Use this before you call the project presentable.

project = {
    "task": "helmet detection",
    "has_data_note": True,
    "has_metric": True,
    "has_failure_case": True,
    "has_annotation_rule": True,
}

ready = all(project[key] for key in ["has_data_note", "has_metric", "has_failure_case", "has_annotation_rule"])

print("task:", project["task"])
print("presentable:", ready)

Expected output:

task: helmet detection
presentable: True

If a project has no annotation rule or failure case, it is still a demo, not a portfolio project.

Learn in This Order

Step	Project Type	Evidence
1	Classification	Dataset split, accuracy/F1, confusion examples
2	Detection	Box annotations, IoU/mAP, false positives and missed detections
3	Segmentation	Masks, IoU/Dice, boundary failures
4	Industry scenario	Risk notes, user impact, deployment idea
5	Hands-on workshop	Reproducible mini pipeline before larger project pages

Run 10.6.4 Hands-on: Build a Reproducible Vision Mini Pipeline before expanding the project.

Project Deliverable Standards

Deliverable	Minimum Requirement	Stronger Portfolio Version
README	Goal, run command, dependencies, examples	Add task boundary, data source, deployment idea
Data and annotation	Image source, class list, annotation format	Add annotation examples, quality checks, bias notes
Results	At least 1 input image and prediction result	Add correct, false positive, false negative, boundary cases
Evaluation	Accuracy, F1, mAP, IoU, Dice, or OCR hit rate	Add error analysis by class, scenario, lighting, clarity
Failure analysis	At least 1 real failure	Add suspected cause, fix action, regression check
Presentation	Screenshot or short GIF proving it runs	Build a clear visual project page

Evidence to Keep

Keep this page’s proof of learning as a small evidence card:

Task Output

classification label, detection box, segmentation mask, OCR text, or video event

Artifacts

original image, processed image, prediction overlay, metrics file, and failure samples

Metric

accuracy/F1, mAP, IoU, Dice, latency, or scenario-specific review score

Failure Check

data quality, label error, preprocessing mismatch, threshold, or deployment constraint

Expected Output

a reproducible run folder with visual outputs and a short failure report

Pass Check

You pass this chapter when your vision project can be reproduced, has clear data and annotation rules, reports proper metrics, and shows where the model fails.

Check reasoning and explanation

1. A passing answer maps the task to the right visual output: class label, bounding box, mask, OCR text, embedding, or video event.
2. The evidence should include a rendered visual artifact and one metric or qualitative error note.
3. A good self-check names one visual failure mode such as class confusion, missed objects, bad masks, lighting shift, domain shift, or weak annotation quality.