I am facing a weird problem which I am not being able to explain. I have an image dataset which I am training on. Training results are quite good and do not seem to be overfitting as validation loss is comparable to training loss.
data = ImageDataBunch.from_csv(path_train, csv_labels=path/'train.csv', ds_tfms=get_transforms(), size=32, bs=128 ).normalize(imagenet_stats) learn = cnn_learner(data, models.resnet34, metrics=error_rate)
The confusion matrix is also quite good.
Now when I am trying to make predictions for the whole training set, my results are pathetic which is really bizarre because this is the same dataset on which I trained my near accurate model.
learn_pred = load_learner(path_train, test=ImageList.from_folder(path_train))
preds_train, y_train, losses_train = learn_pred.get_preds(ds_type=DatasetType.Test, with_loss=True)
The ROC area under curve is under 0.5 and the confusion matrix is very poor.
Next I tried normalizing the data before calling
get_preds() with the same results.
test_data = (ImageList.from_folder(path_train)
I even called
predict() for each individual image and got the same results.
pred =  for img in test_data.train_ds.x: pred.append(learn_pred.predict(img))
I am failing to figure out what I am doing wrong. Would really appreciate if someone could guide me.