So, the question is asking for advice on what to do next, given the model's performance.
Firstly, in terms of confusion matrices, it is often useful to display proportions, rather than number of examples. It makes it easier for us to gauge where the error is occurring.
Looking at the most recent confusion matrix, it is clear that a lot of the examples are ...
Here is how i fixed the classes not corresponding to each other, code is done with Pytorch, most of my function are custom ones but I wrote the equivalent in comments :
modelPath = '../Results/BestModel/model.ntw'
model = Om.openModel(modelPath) # torch.load(modelPath)
dataPath = 'food-101/images' # DataSet path
listClasses = ...
The questions I ask myself when I see learning graphs are the following ones:
Is the loss decreasing and the accuracy increasing ? if yes, your network is learning and everything works fine, which is already a good new.
Have we reached a kind of plateau ? (in accuracy especially), which means learning is over. (Here maybe you could train your network a bit ...
Saying that accuracy is measured to get how accurate the model performs, and F1 is how well the model performs
This doesn't mean anything, it's obviously too vague.
The first things to check in order to understand this relationship are the definitions of accuracy and F1-score.
Wikipedia has a good page which explains how different classification evaluation ...
One option would be to transform the y / target variable to be distributed more like a Gaussian, the most common transformations are log and quantile transformation. Gaussian transformation often increases the model fit statistics.
Taking the difference (ie speed1-speed2) as the target variable effectively dismisses any low-frequency variablitiy and targets only high-frequency variability, even noise.
One approach would be to bin the (highly-variable) target variable into fixed range bins and take the mid point (or any other fixed point) of each bin as the new target (stabilised) ...
There seems to be a confusion between multiclass and multilabel classification:
Multiclass is the regular case where the task consists in predicting among N possible classes. For example an image can be either a dog or a horse or a cat, but always exactly one among these three animals.
Multilabel is the when the task consists in predicting a set. For ...