Image Classification Model
A deep learning image classifier at roughly 96% accuracy, deployed behind a Flask API so the rest of the organization could actually call it.
- Role
- ML Engineer
- Stack
- PythonTensorFlowComputer Vision
Problem
Visual classification was being done by hand at a volume that made it both slow and inconsistent — two people looking at the same image would reasonably disagree. Accuracy mattered, but so did the fact that any solution had to be callable from systems that knew nothing about machine learning.
Solution
We trained a deep learning classifier to roughly 96% accuracy, then spent proportional effort on the part most projects skip: making it usable. A Flask API wrapped the model behind a plain HTTP contract, so integrating teams needed no ML knowledge to consume it. Confidence scores were returned alongside predictions, which let downstream systems decide for themselves when to route to a human — turning a binary classifier into a graded triage tool.
Outcome
Manual review time dropped sharply and classification became consistent across reviewers. Exposing confidence rather than just labels meant the model could be adopted incrementally, with humans staying in the loop exactly where the model was least sure.
Metrics
Bracketed figures are placeholders pending final reporting.
- classification accuracy
- ~96%classification accuracy
- reduction in manual review time
- [XX]%reduction in manual review time
- of images auto-classified above threshold
- [XX]%of images auto-classified above threshold
Approach
The decisions that shaped it.
- Treated deployment as part of the model, not a phase after it.
- Returned calibrated confidence so consumers could set their own risk tolerance.
- Kept the API contract boring and stable so integrations wouldn't break on retraining.
- Held out a genuinely representative evaluation set to keep accuracy honest.
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