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+# Age estimation in supermarkets
+The model analyzed in this card estimates someone's age. This project has been done for the master Applied Artificial Intelligence and is about estimating ages in supermarkets when a person wants to buy alcohol. This model's goal is to only estimate ages in an image. It will not cover ethnicities or gender. 
+
+## Model description
+**Used dataset:** UTKFace images
+- This dataset contains roughly 24K face images.
+- The age of a person on the picture is labeled in the filename of that image. 
+- Since we do not have use for baby images, we decided to cut these out of the dataset, so there are 21K images left.
+
+**Model input:** Facial images
+
+**Model output:** For a face in a picture, the model will return the estimated age of that person. The model output also gives a confidence score for the estimation.
+
+**Model architecture:** A Convolutional Neural Network. This CNN will perform a regression analysis to estimates the ages. 
+
+## Performance 
+
+To determine the performance of the model, the following metrics have been used:
+- MSE, this metric measures how close the regression line is to the data points.
+<br> &ensp; - *Our model's MSE:*
+- RMSE, this metric measures the mean error that can be made.
+<br> &ensp; - *Our model's RMSE:*
+- MAE, this is a measure for model accuracy. The MAE is the average error that the model's predictions have in comparison with their corresponding actual targets.
+<br> &ensp; - *Our model's MAE:*
+
+Ideally, the RMSE and the MAE should be close to each other. When there is a big difference in these two numbers, it is an indication of variance in the individually errors.
+
+Our results show that the prediction model can be ... years off of the actual age of a person.
+
+## Limitations
+- **Lighting**
+<br> When the lighting is poor, the age estimation can be poor as well
+- **Occlusion**
+<br> Partially hidden or obstructed faces might not be detected. (e.g. face masks)
+- **UTKFace** 
+<br> The ages in this dataset are in itself estimation from a previous model. Since we do not know the exact ages of the people in the images, our model will not be the most reliable. 
+
+
+## Training and evaluation data
+
+Train data: 70%
+Test data: 30%
+
+Our model has been made by trial and error. The following architecture is the outcome:
+- Hidden layers:
+- Batch size
+- Epochs: 
+- Optimizer:
+- Activation: 
+