I wonder whether these photos were used for training the model. If that happened and the corresponding BFP sits close to the pictures, the model would probably make the association. Well, if we ignore the privacy questions, one probably could run the same tests with its own photos.
An interesting post! Have you performed any sort of k-fold cross validation?
I've used a 5-fold validation before. Shuffle your images randomly, and then break your data set into 5 subsets. Repeat the training and evaluation steps of your experiment 5 times, each time leaving out a different subset from the training corpus, and then evaluate the trained model against the images that were in the left out set.
Eg, if you shuffle your images and break them out into subsets A through E:
Round 1: Train on A, B, C, D; evaluate model against E.
Round 2: Train on A, B, C, E; evaluate model against D.
Round 3: Train on A, B, D, E; evaluate model against C.
Round 4: Train on A, C, D, E; evaluate model against B.
Round 5: Train on B, C, D, E; evaluate model against A.
From there you can average the results from all 5 rounds to get a better idea of how well your model performs against images it's never seen before.
No, I have not. This was a very crude first test. Feel free to do advanced statistical analyses yourself. It's also a small data set, n = 74. I'm happy to share if relevant.
Since then many befriended bodybuilders have tried it though and it was really impressive in most cases.
This is very interesting!
I wonder whether these photos were used for training the model. If that happened and the corresponding BFP sits close to the pictures, the model would probably make the association. Well, if we ignore the privacy questions, one probably could run the same tests with its own photos.
An interesting post! Have you performed any sort of k-fold cross validation?
I've used a 5-fold validation before. Shuffle your images randomly, and then break your data set into 5 subsets. Repeat the training and evaluation steps of your experiment 5 times, each time leaving out a different subset from the training corpus, and then evaluate the trained model against the images that were in the left out set.
Eg, if you shuffle your images and break them out into subsets A through E:
Round 1: Train on A, B, C, D; evaluate model against E.
Round 2: Train on A, B, C, E; evaluate model against D.
Round 3: Train on A, B, D, E; evaluate model against C.
Round 4: Train on A, C, D, E; evaluate model against B.
Round 5: Train on B, C, D, E; evaluate model against A.
From there you can average the results from all 5 rounds to get a better idea of how well your model performs against images it's never seen before.
No, I have not. This was a very crude first test. Feel free to do advanced statistical analyses yourself. It's also a small data set, n = 74. I'm happy to share if relevant.
Since then many befriended bodybuilders have tried it though and it was really impressive in most cases.