Hi guys, I use resnet to do three binary classification tasks: ADvsNC, MCIvsNC, and ADvsMCI. NC, MCI and AD are normal patients, slight and severe diseases, respectively. For ADvsNC and MCIvsNC, their training processes are reasonable. The training and validation loss reduces and accuracy increases after some epochs. However, for ADvsMCI, the training and validation loss is almost 0 and the accuracy is almost 1.0 at the first epoch. The results are wired, because ADvsMCI is a harder task compared with ADvsNC. It shouldn’t get the best result, especially on the first epoch!
I used the same codes for the three tasks. This means the code has no problems because it works well on the other two tasks. So this means the problem may happen on the dataset.
So I checked the data. I copied the AD from the ADvsNC and MCI from the MCIvsNC to form ADvsMCI. Firstly,  I thought maybe I mixed AD with NC and MCI with NC, so I tried copy NC from ADvsNC and NC from MCIvsNC as AD and MCI, respectively. The training processing is still wired as before. Now I have no idea about this problem.  Could anyone give me some hands? Thank you very much!
PS: I used the trained model to evaluate the training, test, and vald of ADvsMCI, the accuracy is not 1.0.
If I do not make sense, pls let me know