Add data story to presentation

README.md

@@ -49,6 +49,19 @@ without cloud inference APIs.
 Fine-tuned vision model:
 <https://huggingface.co/Lonelyguyse1/halide-vision>
 
+Fine-tuning improved the vision stage where it mattered most for the app:
+structured defect JSON, consistent film-defect labels, scratch and
+emulsion-damage vocabulary, and fewer obvious false positives on clean or
+lookalike regions. The runtime still treats model output as candidate evidence
+and validates every box.
+
+The data bottleneck was central to the build. Public damaged-film examples are
+scattered, noisy, and often not real negatives, so the training curriculum
+combines FilmDamageSimulator annotations, procedural defect positives, synthetic
+scratches and stains, hard clean negatives, and lookalike counterexamples such
+as grass, subject hair, sprocket holes, borders, and glare. The five private
+negatives stayed held out for evaluation only.
+
 Source repository:
 <https://github.com/LonelyGuy-SE1/Project-Halide>