2024 → 2026 准确率曲线,一图看懂

前十名的识别准确率两年内提升了 31 个百分点,而分量准确率提升不到其一半。

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上方标题与摘要已翻译;正文目前为英文,我们正在本地化中。

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.