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Clean up near-duplicate artwork photos with a visual checklist THE PROBLEM I had hundreds of photos of my handmade collages. Some showed the same work photographed at different times, from different angles or against different backgrounds. Others were cropped versions, detail shots or useful making-of photos. The duplicate finder in my image manager missed real matches and suggested unrelated collages instead. Simply keeping one file from each group would also have thrown away useful material. THE WORKFLOW 1. Export a working set and preserve the filenames. I exported the images from Eagle. Smaller copies made the collection easier to upload, while the original-resolution files stayed on my computer. Keeping the filenames intact made it possible to apply decisions to the originals later. 2. Ask AI to compare the artwork, not just the whole photograph. The comparison looked for matching local image details and their relative positions. This helped connect photos despite changes in angle and background. Candidate groups still needed review: two collages might contain the same clipping, and a detail photo might overlap with a wider shot without being redundant. 3. Turn the results into a visual decision page. The AI produced a standalone HTML file with photos grouped side by side and their original filenames underneath. I requested a “Keep” checkbox beneath each candidate and a category dropdown: “Collage” or “Making of”. Multiple images could stay in either category. One photo might show one part clearly while another preserved a different part better. I made the decisions; the AI did not choose a winner automatically. 4. Validate and export my choices. The page flagged any group with nothing selected and any selected photo without a category. Photos without a confirmed match were preserved separately. A download button exported my decisions as a small JSON file, which I sent back to the AI. 5. Generate a local cleanup script from that exact selection. The resulting Windows script checks the expected files, asks me to confirm the folder, sorts the retained photos into “collages”, “making of” and “loose items”, and sends rejected photos to the Recycle Bin. It targets the ordinary export folder, not Eagle’s internal library. The loose items can then be sorted manually before reimporting. WHAT THIS PRODUCED From 296 photos, the review grouped 204 into 90 candidate groups and left 92 without a confirmed match. My final choices retained 96 collage photos and 23 making-of photos, plus all 92 loose items: 211 retained, 85 designated for removal. The checklist and selection were completed, and the script was generated. At the time of writing, I had not yet run the cleanup on Windows. WHY IT HELPED The key was separating three jobs: finding likely matches, deciding what matters, and applying those decisions to files. “The same artwork” does not necessarily mean “an unnecessary photo”. A visual checklist let me make those distinctions without manually tracking filenames. STARTER PROMPT “I have photos of the same artwork taken from different angles and against different backgrounds. Find candidate matches using details within the artwork, and distinguish duplicates from details, changed versions and making-of photos. Create a standalone HTML review page with original filenames, a Keep checkbox and a category dropdown for each candidate. Allow multiple retained photos per category. Flag empty groups and selected photos with no category. Preserve unmatched images. Let me export my choices as JSON. After I return the selection, generate a script that validates the original filenames, sorts retained files into the chosen folders and moves only rejected files to the Recycle Bin after showing the target folder and counts. Do not choose or delete files on my behalf."