ResumeProof

Role vocabulary

ATS resume keywords for Computer Vision

These are the 28 terms our scorer checks a Computer Vision resume against. It is the same list the free check uses, published in full.

Check my resume against these Build a Computer Vision resume

The vocabulary

Coverage is 25 of the 100 points. Sixty percent of this list is full marks, so you do not need all of it, and stuffing every term in is penalised separately.

  • computer vision
  • image processing
  • object detection
  • image classification
  • image segmentation
  • opencv
  • yolo
  • cnn
  • vision transformer
  • pytorch
  • tensorflow
  • python
  • c++
  • cuda
  • tensorrt
  • onnx
  • data augmentation
  • annotation
  • ocr
  • object tracking
  • video analytics
  • edge deployment
  • camera calibration
  • point cloud
  • quantization
  • model optimisation
  • mean average precision
  • inference

The eight questions for Computer Vision

The guided builder asks these, in this order, and turns each answer into a line on the page. Nothing is written that you did not type.

  1. 1.How many vision models or systems have you shipped to production, and what do they detect or classify?
  2. 2.Which accuracy metric did your best model move, such as mAP, IoU or F1, and by how much?
  3. 3.How many images or hours of video do your models process, per day or in total?
  4. 4.Which frameworks, libraries and hardware do you use daily?
  5. 5.What latency or frame rate does your model achieve, and on which device?
  6. 6.How large are your training datasets, and how were they labelled?
  7. 7.How much time, cost or manual inspection has your system saved?
  8. 8.What is the hardest vision problem you have solved, and how?
Answer them and see your score before paying

Words are the easy half

A resume can carry every term above and still be rejected, because coverage says what you know and nothing about what you delivered. The heaviest category on the rubric is evidence: how many of your bullets carry an actual figure.

Seven in ten is full marks. Most resumes we see manage two.

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