learn.lr_find() computes only on one Epoch in your case (see the 1/1 after the 100% after the green bar). It means that the plot between LR vs Loss will have only one point. So it is blank since no curves are drawn.
Since the dataset has more elements than a batchsize, you have enough pictures. It doesn’t come from that.
“GENDER-FERET dataset is a balanced subset of the FERET dataset, adapted for gender recogntion purposes. It consists of 946 grayscale images, already divided in training set (237 m, 237 f) and test set (236 m, 236 f).” according to your link (http://mivia.unisa.it/datasets/video-analysis-datasets/gender-recognition-dataset/).
But fast.ai library works with train set and validation set. The test set is only for Kaggle competition submission. It is probably an error coming from the name of your paths which should be “train” and “valid” by default. (Be sure that your train set and validation set are labelled thanks to path per gender (i.e. “m” and “f”).) So look at your ImageClassifierData.from_paths by typing in your Jupyter Notebook :
I hope it helps.