File size: 1,112 Bytes
155e5dc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 |
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
|