Duplicate Image Finder
Find exact duplicates and visually similar images without deleting anything.
Read the guide and worked example ↓
No settings are required. The tool will create a safe report or output without changing your original file.
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The download appears directly below its progress bar when processing finishes.
How to use Duplicate Image Finder
Find exact duplicate files and groups with matching compact visual hashes. The report helps you investigate redundancy before annotation or splitting. It never deletes images automatically, and a visual match is a review candidate rather than proof of identical scene content.
Prepare, run, and review
- Upload a ZIP of supported images. Exact matching compares file bytes, while visual matching decodes the image, converts it to grayscale, and compares a small difference-hash representation.
- Run the tool and download duplicate_image_report.zip. The JSON summarizes groups, and a CSV is included when there are flagged rows.
- Inspect groups marked exact separately from groups marked visually_similar. The current visual check groups identical difference hashes; it is not an exhaustive nearest-neighbor search with adjustable similarity thresholds.
- Review the original images and annotations before removing or moving anything. Preserve examples that carry distinct labels, metadata, viewpoints, or useful variation.
Download a small, synthetic example dataset. It contains four labeled rectangle images and a matching copy of the first image for duplicate-check practice. It is for learning the file workflow, not for training a useful detector.
An illustrative report row
type: exact
group: shared SHA-256 digest
file: images/copy_of_frame_001.jpgAn exact group means the files share the same byte digest. Two differently compressed copies of the same photograph may not be exact matches, while visually different images can occasionally share a compact visual hash. A file can appear in both an exact group and a visual group, so flagged row totals should not automatically be read as a count of unique files.
Limits and common mistakes
- Matching visual hashes can miss near-duplicates changed by cropping, rotation, viewpoint, or other transformations. The absence of a group does not prove independence.
- A visual hash can collide: images with similar coarse brightness structure may be grouped even when their detailed content differs.
- Exact duplicate removal does not solve all train-test leakage. Related scenes and adjacent video frames may still share too much information.
- An unreadable image cannot contribute a trustworthy visual comparison. Use Dataset Health Check to inspect decoding problems.
Does visually_similar mean the files are byte-for-byte identical?
No. It means the current compact visual hashes match. Open both images and inspect them before deciding whether one is redundant.
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ToolsInVision guide · Updated September 26, 2026 · Editorial approach · Report an issue