Topic 5 of 8

Loss Functions For Vision Tasks

Learn Loss Functions For Vision Tasks for free with explanations, exercises, and a quick test (for Computer Vision Engineer).

Published: January 5, 2026 | Updated: January 5, 2026

Who this is for

Learning path

Before this: Data preprocessing and augmentation; network basics; optimization (LR schedules, regularization).
This subskill: Choosing and tuning loss functions for vision tasks.
After this: Advanced optimization (contrastive objectives, curriculum learning), task-specific metrics and calibration.

Next steps

Pick one of the Practical projects and run a small ablation on your data.
Create a simple “loss decision sheet” for your team with defaults and when-to-switch notes.
Log per-head gradients for one training run and adjust weights once.

Mini challenge

You have a detection dataset with many easy negatives, rare positives, and small objects. In one paragraph, propose your loss stack (classification and box), initial weights, and how you will know when to reweight. Keep it concise and actionable.

Practice Exercises

2 exercises to complete

Instructions

You have 10,000 images: 1,000 positives (defect) and 9,000 negatives (no defect). Choose a loss approach and provide parameters that address class imbalance and overconfidence.

Pick between Weighted BCE, Focal Loss, or a combination.
Provide numeric weights/parameters (e.g., pos_weight, alpha, gamma, label smoothing).
Explain why your choice should improve recall without tanking precision.

Expected Output

A short plan listing the chosen loss, parameter values (e.g., pos_weight=9, gamma=2, alpha=0.25), and a 2–3 sentence rationale.