In this project, my group mates and I built Food Calories Estimation System using with machine learning methods.
Course Web Page: BBM 406 Fall 2018 Fundamentals of Machine Learning
You can see what we did week by week with more details.
- https://medium.com/bbm406f18/week-1-seefood-be1097c7876a
- https://medium.com/bbm406f18/week-2-seefood-ae381ea34757
- https://medium.com/bbm406f18/week-3-seefood-a511dd2f17a7
- https://medium.com/bbm406f18/week-4-seefood-59f1b759b173
- https://medium.com/bbm406f18/week-5-seefood-f495a76ded70
- https://medium.com/bbm406f18/week-6-seefood-52720a73823d
- https://medium.com/bbm406f18/week-7-seefood-959bc06ec32
We used ECUST Food Dataset. You can see more information about our used dataset ECUSTFD.
We used Faster R-CNN model to detect foods. We compare few different models then we obtained best results at Facter R-CNN inception v2 coco.
Our baseline project used mathmatics formulas. We thought we can do better. Then we use Machine Learning Algorithms to estimate calories. We used Random Forest and K-Nearest Neighbors methods when we calculate calories.
| Method Name | Volume RMSE | Calorie RMSE |
|---|---|---|
| KNN | 21.06 | 45.69 |
| Random Forest | 13.21 | 30.37 |
You can see more result and information about our project in our final report