De nauwkeurigheidscurve 2024 → 2026, in grafieken
De identificatienauwkeurigheid van de top 10 is in twee jaar 31 procentpunt gestegen. De portienauwkeurigheid minder dan de helft.
Kop en samenvatting hierboven zijn vertaald; het volledige artikel hieronder is voorlopig in het Engels terwijl we de lopende tekst lokaliseren.
We have run this benchmark, in some form, since Q1 2024. That gives us a two-year window, enough to see where the field is actually moving versus where it is just marketing.
Identification: solved-ish
Across the top 10, mean identification accuracy went from 53.1% (Q1 2024) to 84.3% (Q1 2026), a 31-point gain. Most of that came from two model generations of vision-language progress, not from food-specific innovation.
The takeaway: if your app still can’t tell pad thai from yakisoba, it’s not a food AI problem anymore, it’s a “this team didn’t upgrade their model” problem.
Portion: still hard
Mean portion error dropped less than half as fast, from ±18.4% to ±7.9% in the same window. And the distribution is bimodal. The top three apps cluster between ±1 and ±5%. The rest of the field is still above ±7%.
The leader, Welling, is roughly five years ahead of the bottom of the chart on this single metric.
Why the gap will widen before it closes
Portion estimation rewards proprietary data and depth fusion. Both are expensive to build and hard to copy from a research paper. Identification rewards using the best available foundation model, which everyone can do.
Expect identification to keep converging at the top of the leaderboard, and portion accuracy to keep separating the leaders from the field.