diff --git a/examples/dedup/README.md b/examples/dedup/README.md
index eab6b58..965f0e1 100644
--- a/examples/dedup/README.md
+++ b/examples/dedup/README.md
@@ -32,3 +32,51 @@ The script uses `pooled` (`[cls || mean(patches)]`) when present and falls
 back to `cls`. For large folders the pairwise pass is O(n^2) in Python; a
 few thousand images is fine, hundreds of thousands will want a real index
 (FAISS, sqlite-vec, ...).
+
+Caveat: `.webp` is listed in `EXTS` but `dinov2-cli` cannot decode WebP
+(`stb_image` has no WebP support). A single `.webp` file in the scanned
+folder makes the CLI exit non-zero and the script aborts. Keep the input
+folder to JPEG/PNG/BMP/TGA until that is fixed.
+
+## Measured run (synthetic corpus, planted duplicates)
+
+Corpus: 320 photos from `picsum.photos` (`/seed/<n>/400/300`, seeds 1..320,
+all JPEG) plus 40 planted variants of 30 of them (every source got a JPEG
+recompress at q=60; 10 sources also got an 80% resize, a 90% center crop,
+or a +15% brightness shift). Picsum maps seeds onto a limited photo pool,
+so the download itself already contained natural duplicates; ground truth
+was therefore built from pixel-identical groups of the originals (46 groups
+covering 98 files) plus the planted variants, giving 64 true duplicate
+clusters and 130 true duplicate pairs across 360 files.
+
+Command (Apple M1, Metal, `dinov2-small`):
+
+```bash
+/usr/bin/time -l python3 examples/dedup/dedup.py \
+  ./build-release/bin/dinov2-cli models/dinov2-small/model.gguf \
+  /tmp/dedup-corpus/images -o report.html
+```
+
+Result: 360 images embedded in 124 s wall clock (~0.35 s/image), peak RSS
+~335 MB, 64 clusters reported at the default `-t 0.95`.
+
+Scored pair-level against ground truth (predicted pairs are all pairs
+inside reported clusters):
+
+| threshold | clusters | precision | recall | F1    |
+|-----------|----------|-----------|--------|-------|
+| 0.999     | 60       | 1.000     | 0.646  | 0.785 |
+| 0.99      | 64       | 1.000     | 0.846  | 0.917 |
+| 0.975     | 64       | 1.000     | 0.915  | 0.956 |
+| 0.95      | 64       | 1.000     | 0.977  | 0.988 |
+| 0.90      | 63       | 0.935     | 1.000  | 0.967 |
+| 0.85      | 63       | 0.867     | 1.000  | 0.929 |
+
+On this corpus the default `0.95` is the sweet spot: zero false merges,
+and the only miss is one 90% center crop that lands at ~0.93 similarity.
+Raising the cutoff toward `0.99` keeps precision perfect but drops recall
+to 0.85 (crops and some resizes fall under the bar); lowering it to `0.90`
+reaches perfect recall at the cost of 9 false pairs from merely similar
+photos.
+
+![sanitized sample of the HTML report](report-sample.png)
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