0.5.7

• Added mAP to training graph visualization
• Added following options to config: valid_metric(choice of val_loss/val_accuracy for classifier and segnet, and val/loss/mAP for detector), backend weights(imagenet/None/path to backend weight file), save_bottleneck(only for classifier, True/False, saves bottlneck weights after training is finished to the project folder. Later the weights can be used as backend weights for the model with the same backend --- i.e. train a classifier model, save bottleneck weights and then load them for training of detector/segnet model).
• Experimental Edge TPU conversion (only tested with Mobilenet classifier now)
• Fixed preprocessing for inference (different backends use different image preprocessing, to find out more, search for "keras application preprocessing" or look inside of feature.py file in aXeleRate)