i.ann.maskrcnn.train - GRASS GIS manual

Note: This document is for an older version of GRASS GIS that will be discontinued soon. You should upgrade, and read the current manual page.

Note: This addon document is for an older version of GRASS GIS that will be discontinued soon. You should upgrade your GRASS GIS installation, and read the current addon manual page.

NAME

i.ann.maskrcnn.train - Train your Mask R-CNN network

KEYWORDS

ann, vector, raster

SYNOPSIS

i.ann.maskrcnn.train

i.ann.maskrcnn.train --help

i.ann.maskrcnn.train [-esbn] training_dataset=name [model=name] classes=string[,string,...] logs=name name=string [epochs=integer] [steps_per_epoch=integer] [rois_per_image=integer] [images_per_gpu=integer] [gpu_count=integer] [mini_mask_size=integer[,integer,...]] [validation_steps=integer] [images_min_dim=integer] [images_max_dim=integer] [backbone=string] [--help] [--verbose] [--quiet] [--ui]

Flags:

-e: Pretrained weights were trained on another classes / resolution / sizes
-s: Do not use 10 % of images and save their list to logs dir
-b: Train also batch normalization layers (not recommended for small batches)
-n: No resizing or padding of images (images must be of the same size)
--help: Print usage summary
--verbose: Verbose module output
--quiet: Quiet module output
--ui: Force launching GUI dialog

Parameters:

training_dataset=name [required]: Path to the dataset with images and masks; Name of input directory
model=name: Path to the .h5 file to use as initial values; Keep empty to train from a scratch
classes=string[,string,...] [required]: Names of classes separated with ","
logs=name [required]: Path to the directory in which will be models saved; Name of input directory
name=string [required]: Name for output models
epochs=integer: Number of epochs; Default: 200
steps_per_epoch=integer: Steps per each epoch; Default: 3000
rois_per_image=integer: How many ROIs train per image; Default: 64
images_per_gpu=integer: Number of images per GPU; Bigger number means faster training but needs a bigger GPU; Default: 1
gpu_count=integer: Number of GPUs to be used; Default: 1
mini_mask_size=integer[,integer,...]: Size of mini mask separated with ","; To use full sized masks, keep empty. Mini mask saves memory at the expense of precision
validation_steps=integer: Number of validation steps; Bigger number means more accurate estimation of the model precision; Default: 100
images_min_dim=integer: Minimum length of images sides; Images will be resized to have their shortest side at least of this value (has to be a multiple of 64); Default: 256
images_max_dim=integer: Maximum length of images sides; Images will be resized to have their longest side of this value (has to be a multiple of 64); Default: 1280
backbone=string: Backbone architecture; Options: resnet50, resnet101; Default: resnet101

DESCRIPTION
- DATASET STRUCTURE
NOTES
EXAMPLES
SEE ALSO
AUTHORS

DESCRIPTION

i.ann.maskrcnn.train allows the user to train a Mask R-CNN model on his own dataset. The dataset has to be prepared in a predefined structure.

DATASET STRUCTURE

Training dataset should be in the following structure:

dataset-directory

imagenumber
- imagenumber.jpg (training image)
- imagenumber-class1-number.png (mask for one instance of class1)
- imagenumber-class1-number.png (mask for another instance of class1)
- ...
imagenumber2
- imagenumber2.jpg
- imagenumber2-class1-number.png (mask for one instance of class1)
- imagenumber2-class2-number.png (mask for another class instance)
- ...

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.

NOTES

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

images_max_dim x images_max_dim

. It results in the fact that even images of different sizes may be used.

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).

EXAMPLES

Dataset for examples:

crops

000000
- 000000.jpg
- 000000-corn-0.png
- 000000-corn-1.png
- ...
000001
- 000001.jpg
- 000001-corn-0.png
- 000001-rice-0.png
- ...

Training from scratch

i.ann.maskrcnn.train training_dataset=/home/user/Documents/crops classes=corn,rice logs=/home/user/Documents/logs name=crops

After default number of epochs, we will get a model where the first class is trained to detect corn fields and the second one to detect rice fields.

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

Training from a pretrained model

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

Fine-tuning a model

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

AUTHORS

Ondrej Pesek

SOURCE CODE

Available at: i.ann.maskrcnn.train source code (history)

Latest change: Monday Jun 28 07:54:09 2021 in commit: 1cfc0af029a35a5d6c7dae5ca7204d0eb85dbc55