Add maskal config

maskal_ws/config/classes.yaml

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+# training-parameters
+classes: ['person', 'roboone']
+# train-sampler
+minority_classes: ['person', 'roboone']

maskal_ws/config/directory.yaml

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+# folders
+weightsroot: "~/lib/roboone_auto_dataset/maskal_ws/weights"
+resultsroot: "~/lib/roboone_auto_dataset/maskal_ws/results"
+dataroot: "~/lib/roboone_auto_dataset/maskal_ws/datasets"
+initial_train_dir: "~/lib/roboone_auto_dataset/maskal_ws/datasets/initial_train"
+traindir: "~/lib/roboone_auto_dataset/maskal_ws/datasets/train"
+valdir: "~/lib/roboone_auto_dataset/maskal_ws/datasets/val"
+testdir: "~/lib/roboone_auto_dataset/maskal_ws/datasets/test"
+
+# data options
+use_initial_train_dir: False
+duplicate_initial_model_and_data: False
+initial_train_file: ""

maskal_ws/config/maskal.yaml

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+# Parameters for maskAL
+# python maskAL.py --config maskAL.yaml
+# explanation can be found here: https://github.com/pieterblok/maskal
+
+# training-parameters
+cuda_visible_devices: '0'
+transfer_learning_on_previous_models: True
+learning_rate: 0.01
+warmup_iterations: 500
+train_iterations_base: 2500
+train_iterations_step_size: 2500
+step_image_number: 500
+eval_period: 500
+checkpoint_period: -1
+weight_decay: 0.0001
+learning_policy: 'steps_with_decay'
+step_ratios: [0.5, 0.8]
+gamma: 0.1
+train_batch_size: 1
+num_workers: 2
+
+# train-sampler
+train_sampler: "TrainingSampler"
+repeat_factor_smallest_class: 1.0
+
+# evaluation-parameters
+confidence_threshold: 0.5
+nms_threshold: 0.2
+
+# active-learning sampling
+experiment_name: 'exp1'
+strategy: 'uncertainty'
+mode: 'mean'
+initial_datasize: 5
+pool_size: 5
+equal_pool_size: True
+loops: 5
+dropout_probability: 0.25
+mcd_iterations: 10
+iou_thres: 0.5
+auto_annotate: True
+export_format: labelme
+supervisely_meta_json: "./datasets/meta.json"

maskal_ws/config/network.yaml

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+### Network Parameters
+### Standard
+# network_config: COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml
+# pretrained_weights: COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml
+
+### Light-weight
+network_config: COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml
+pretrained_weights: COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml