Training dataset should be in the following structure:
The described structure of directories is required. Pictures must be *.jpg files with 3 channels (for example RGB), masks must be *.png files consisting of numbers between 1 and 255 (object instance) and 0s (elsewhere). A mask file for each instance of an object should be provided separately distinguished by the suffix number.
If you are using initial weights (the model parameter), epochs are divided into three segments. Firstly training layers 5+, then fine-tuning layers 4+ and the last segment is fine-tuning the whole architecture. Ending number of epochs is shown for your segment, not for the whole training.
The usage of the -b flag will result in an activation of batch normalization layers training. By default, this option is set to False, as it is not recommended to train them when using just small batches (batch is defined by the images_per_gpu parameter).
If the dataset consists of images of the same size, the user may use the -n flag to avoid resizing or padding of images. When the flag is not used, images are resized to have their longer side equal to the value of the images_max_dim parameter and the shorter side longer or equal to the value of the images_min_dim parameter and zero-padded to be of shape
After each epoch, the current model is saved. It allows the user to stop the training when he feels satisfied with loss functions. It also allows the user to test models even during the training (and, again, stop it even before the last epoch).
Dataset for examples:
i.ann.maskrcnn.train training_dataset=/home/user/Documents/crops classes=corn,rice logs=/home/user/Documents/logs name=crops
If we use the command with reversed classes order, we will get a model where the first class is trained to detect rice fields and the second one to detect corn fields.
i.ann.maskrcnn.train training_dataset=/home/user/Documents/crops classes=rice,corn logs=/home/user/Documents/logs name=crops
The name of the model does not have to be the same as the dataset folder but should be referring to the task of the dataset. A good name for this one (referring also to the order of classes) could be also this one:
i.ann.maskrcnn.train training_dataset=/home/user/Documents/crops classes=rice,corn logs=/home/user/Documents/logs name=rice_corn
We can use a pretrained model to make our training faster. It is necessary for the model to be trained on the same channels and similar features, but it does not have to be the same ones (e.g. model trained on swimming pools in maps can be used for a training on buildings in maps).
A model trained on different classes (use -e flag to exclude head weights).
i.ann.maskrcnn.train training_dataset=/home/user/Documents/crops classes=corn,rice logs=/home/user/Documents/logs name=crops model=/home/user/Documents/models/buildings.h5 -e
A model trained on the same classes.
i.ann.maskrcnn.train training_dataset=/home/user/Documents/crops classes=corn,rice logs=/home/user/Documents/logs name=crops model=/home/user/Documents/models/corn_rice.h5
It is also possible to stop your training and then continue. To continue in the training, just use the last saved epoch as a pretrained model.
i.ann.maskrcnn.train training_dataset=/home/user/Documents/crops classes=corn,rice logs=/home/user/Documents/logs name=crops model=/home/user/Documents/models/mask_rcnn_crops_0005.h5
Latest change: Thursday Feb 03 09:32:35 2022 in commit: f17c792f5de56c64ecfbe63ec315307872cf9d5c
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