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Library/ Deep Learning/ Module 2 · Project 1: Facial Emotion Detection (image classification with CNN)/ 2.1 Understand the problem

subtopic

Define the product and how we will judge it

10 min applied30 min research1 curated resources

Write down: input (a face image), output (one of 7 emotions + confidence), users (e.g. customer-feedback kiosk, e-learning engagement). Decide the score before training: accuracy and per-emotion recall on the test set. Reality check: even humans agree only about 65% of the time on this dataset, so a good model scores about 65–70%.

Builds on: E01 · Build: Problem Framing Brief (one page per project), M02 · Build: Project Understanding, Data Collection & Data Synthesis

Notes, key code, what to skip and 1 curated resources are inside the platform.Join the waitlist and we'll build you a roadmap that includes this topic only if your goal needs it.

Define the product and how we will judge it · 1x1labs.ai