MODEL: | |
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" | |
RESNETS: | |
DEPTH: 101 | |
META_ARCHITECTURE: "RetinaNet" | |
BACKBONE: | |
NAME: "build_retinanet_resnet_fpn_backbone" | |
RESNETS: | |
OUT_FEATURES: ["res3", "res4", "res5"] | |
ANCHOR_GENERATOR: | |
SIZES: !!python/object/apply:eval ["[[x, x * 2**(1.0/3), x * 2**(2.0/3) ] for x in [32, 64, 128, 256, 512 ]]"] | |
FPN: | |
IN_FEATURES: ["res3", "res4", "res5"] | |
RETINANET: | |
IOU_THRESHOLDS: [0.4, 0.5] | |
IOU_LABELS: [0, -1, 1] | |
SMOOTH_L1_LOSS_BETA: 0.0 | |
ROI_HEADS: | |
NUM_CLASSES: 20 | |
DATASETS: | |
TRAIN: ("fathomnet_train",) | |
TEST: ("fathomnet_val",) | |
SOLVER: | |
IMS_PER_BATCH: 4 | |
BASE_LR: 0.001 # Note that RetinaNet uses a different default learning rate | |
LR_SCHEDULER_NAME: WarmupMultiStepLR | |
STEPS: (108000, 216000, 324000) | |
MAX_ITER: 432000 | |
WARMUP_FACTOR: 0.001 | |
WARMUP_ITERS: 5000 | |
WARMUP_METHOD: linear | |
GAMMA: 0.1 | |
INPUT: | |
MIN_SIZE_TRAIN: (640, 720, 800) | |
MAX_SIZE_TRAIN: 1422 | |
MIN_SIZE_TEST: 720 | |
MAX_SIZE_TEST: 1280 | |
VERSION: 2 | |
TEST: | |
AUG: | |
MIN_SIZES: (640, 720, 800) | |
MAX_SIZE: 1422 | |
DETECTIONS_PER_IMAGE: 300 | |