lanenet.yaml

EVAL_CROP_SIZE: (512, 256) # (width, height), for unpadding rangescaling and stepscaling
TRAIN_CROP_SIZE: (512, 256) # (width, height), for unpadding rangescaling and stepscaling
AUG: 
    AUG_METHOD: u"unpadding" # choice unpadding rangescaling and stepscaling
    FIX_RESIZE_SIZE: (512, 256) # (width, height), for unpadding
    INF_RESIZE_VALUE: 500  # for rangescaling
    MAX_RESIZE_VALUE: 600  # for rangescaling
    MIN_RESIZE_VALUE: 400  # for rangescaling
    MAX_SCALE_FACTOR: 2.0  # for stepscaling
    MIN_SCALE_FACTOR: 0.5  # for stepscaling
    SCALE_STEP_SIZE: 0.25  # for stepscaling
    MIRROR: False
    RICH_CROP: 
        ENABLE: False 

BATCH_SIZE: 4

DATALOADER: 
    BUF_SIZE: 256
    NUM_WORKERS: 4
DATASET: 
    DATA_DIR: "./dataset/tusimple_lane_detection"
    IMAGE_TYPE: "rgb"  # choice rgb or rgba
    NUM_CLASSES: 2
    TEST_FILE_LIST: "./dataset/tusimple_lane_detection/training/val_part.txt"
    TEST_TOTAL_IMAGES: 362
    TRAIN_FILE_LIST: "./dataset/tusimple_lane_detection/training/train_part.txt"
    TRAIN_TOTAL_IMAGES: 3264
    VAL_FILE_LIST: "./dataset/tusimple_lane_detection/training/val_part.txt"
    VAL_TOTAL_IMAGES: 362
    SEPARATOR: " "
    IGNORE_INDEX: 255

FREEZE:
    MODEL_FILENAME: "__model__"
    PARAMS_FILENAME: "__params__"
MODEL:
    MODEL_NAME: "lanenet"
    DEFAULT_NORM_TYPE: "bn"
TEST:
    TEST_MODEL: "./saved_model/lanenet/final/"
TRAIN:
    MODEL_SAVE_DIR: "./saved_model/lanenet/"
    PRETRAINED_MODEL_DIR: "./pretrained_models/VGG16_pretrained"
    SNAPSHOT_EPOCH: 1
SOLVER:
    NUM_EPOCHS: 100
    LR: 0.0005
    LR_POLICY: "poly"
    OPTIMIZER: "sgd"
    WEIGHT_DECAY: 0.001