The test dataset is a little imbalanced. 1,700,000 for the first class, and  1,000,000 for the second.
I’m plotting it sequentially, logging each validation batch, but I do calculate the average and print it. It it about 50%.
Plotting part:
...
from tensorboardX import SummaryWriter
writer = SummaryWriter()

...

        if i % args.print_freq == 0:
            print('Epoch: [{0}][{1}/{2}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Prec@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
                   epoch, i, len(train_loader), batch_time=batch_time,
                   data_time=data_time, loss=losses, top1=top1))
        writer.add_scalar('loss', loss.item(), globaliter_train/args.print_freq)
        writer.add_scalar('Accuracy', top1.val, globaliter_train/args.print_freq)
        globaliter_train += 1