Short version: photo-based trackers are usually within 10 to 20% of a weighed-and-measured baseline on standard meals. That sounds loose, and it is. It also happens to be the same accuracy band as careful manual logging by someone who is not weighing their food. The math is rarely the bottleneck. Logging at all is.
Where the model is right
Single, recognisable plates with clean separation are easy. A grilled chicken breast, half a cup of rice, and steamed broccoli get identified and portioned within a few grams. Same with packaged foods photographed next to the label, and most fast-food items where the chain has trained the model on standard sizes.
Where the model is wrong
Mixed plates with hidden ingredients are the failure mode. A curry where you cannot see the oil, a casserole with embedded cheese, a salad with dressing already tossed through. The portion estimate is also softer on composite carbs (rice piled high vs rice spread flat reads as the same weight to a camera). Most trackers under-count fat in these cases by 5 to 15%.
How to use one without lying to yourself
Three rules that close most of the gap, none of which require a kitchen scale:
- Photograph before you mix. If you can shoot the plate before sauce, log it then. You can always add the sauce as a second entry.
- Adjust portions when they look off. If the model shows a small portion and you ate a large one, drag the size up. Most apps let you do this in one tap. A 20% portion adjustment closes a 200-calorie gap on a 1,000-calorie meal.
- Track for two weeks, then read the trend. Your weight trend after 14 days tells you whether the camera estimates are working for you. If you are losing on a 500 kcal/day estimated deficit, the math is good enough.
Compared to weighing every meal
A kitchen scale gets you within 3 to 5% of the truth. The catch is what happens to adherence: most people who try scale-based tracking quit within ten days. A camera-based approach that gets you within 15% and that you keep using is dramatically more useful than a scale-based approach you abandon.
What healmeal does specifically
healmeal sends your photo to a vision model, returns identified items with portion estimates, and lets you adjust before saving. We surface the model's confidence implicitly through the spread of identified items: a confident answer collapses to one dish, a less confident one offers components you can edit individually. You can always shoot it again from a different angle if the first read looks off.
If you have not tried a camera-based tracker yet, the easiest no-risk check is to drop a photo into our web demo and see how close the estimate lands.
