The best computer vision projects for an internship application are small but complete: they take real images or video, run a model, and show useful output — with a short write-up of what went wrong and how you fixed it. Two solid projects beat ten tutorial copies.
Object detection on your own phone footage (e.g. counting vehicles or people); image classification on a dataset you collected yourself; a simple face-blurring or licence-plate-blurring tool with OpenCV; or measuring a model's speed versus accuracy on CPU.
A short README with the problem, your approach, results (with a few images), and — most importantly — what failed and what you changed. That last part shows the production mindset interviewers are looking for.
Copy-pasted tutorials with no changes, and projects that only show a final accuracy number with no explanation.
Students and freshers preparing to apply for computer vision internships who want something concrete to show.
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