Dog breed photo search
SmartBreeds.io
Upload a dog photo, see the top breed match with confidence, and compare the closest alternatives before opening the breed guide.
Upload flow
The prediction stays above the fold on mobile.
The app page keeps the top breed name, confidence, and closest alternatives first. Longer research notes live on the research pages, not inside the user-facing upload result.
- Top breed, confidence, and alternatives are the first result block.
- The restart action sits below the prediction summary.
- Research details are linked through the case study and report.
96-second research preview
The case study embeds the video with captions, the charts, and the weak-class tables.
Measured, not inflated
The useful story is narrower than a product claim.
SmartBreeds is not presented as a final benchmark or a deployed guarantee. The result is a reproducible calibration study with visible failure modes.
Reliability visuals
Charts are part of the claim boundary.
These figures are public-safe research visuals. Dataset-derived dog photos stay private until the license review is complete.
Reliability readout
| Quantity | Value | Scope |
|---|---|---|
| ECE | 0.0508 | 2,000-image test split |
| Top-1 | 0.8455 | DINOv2-small prototypes |
| 0.9-1.0 bin | 0.968 confidence / 0.976 accuracy | 918 predictions |
Lowest global RAPS classes
| Breed | Coverage | Mean set size |
|---|---|---|
| great_dane | 0.80 | 3.45 |
| lhasa | 0.85 | 2.35 |
| tibetan_mastiff | 0.85 | 2.50 |
Next research gate
Weak-class coverage gets priority over bigger claims.
The next work targets lhasa, tibetan mastiff, great dane, and the worst Mondrian class. The research question is whether structured pooling can tighten sets without burying class-specific failures.
Product routes