Validation accuracy for object detection on custom dataset (SSD model)

Hi,
I’m trying to finetune an SSD model to detect a certain object in a custom dataset.

I downloaded the images, created the .lst files, and the corresponding train.rec and test.rec files following the tutorial. I first used a slightly modified version of finetune_detection.py and everything works ok, but this way the accuracy on the validation set is not provided.

I would like to track it in order to decide when to stop the training, and I seem to understand that the validation accuracy is calculated when using the full train_ssd.py script.
However, I’m having trouble figuring out how to set the right arguments to run it on my custom dataset.

I tried something in the line of:

python train_ssd.py --data-shape=512  --dataset-root=custom_dataset_path/ --epochs=10

but I’m getting the error:

OSError: ~/.mxnet/datasets/voc is not a valid dir. Did you forget to initialize datasets described in [...]

Any suggestions? Thanks!