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_BASE_: "./Base-RCNN-C4.yaml"
MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
BACKBONE:
NAME: "build_clip_resnet_backbone" #"build_clip_resnet_fpn_backbone" # "build_resnet_fpn_backbone"
FREEZE_AT: 2
WEIGHTS: "" # "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50
OUT_FEATURES: ["res4"]
NORM: FrozenBN
STEM_OUT_CHANNELS: 64
RES2_OUT_CHANNELS: 256
RPN:
HEAD_NAME: StandardRPNHead
IN_FEATURES: ["res4"]
ROI_HEADS:
NAME: "CLIPRes5ROIHeads" # "Res5ROIHeads" # "StandardROIHeads"
IN_FEATURES: ["res4"]
NUM_CLASSES: 1203
SCORE_THRESH_TEST: 0.0001
ROI_BOX_HEAD:
NAME: ""
NUM_FC: 0
POOLER_RESOLUTION: 14
ROI_MASK_HEAD:
NAME: "MaskRCNNConvUpsampleHead"
NUM_CONV: 0
POOLER_RESOLUTION: 14
PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] # [103.530, 116.280, 123.675] #
PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] # [1.0, 1.0, 1.0] #
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
DATASETS:
TRAIN: ("lvis_v1_train",)
TEST: ("lvis_v1_val",)
TEST:
DETECTIONS_PER_IMAGE: 300 # LVIS allows up to 300
EVAL_PERIOD: 25000
SOLVER:
IMS_PER_BATCH: 16
BASE_LR: 0.02
STEPS: (120000, 160000) # (140000,) #
MAX_ITER: 180000 # 180000 * 16 / 100000 ~ 28.8 epochs
DATALOADER:
SAMPLER_TRAIN: "RepeatFactorTrainingSampler"
REPEAT_THRESHOLD: 0.001
INPUT:
MIN_SIZE_TRAIN_SAMPLING: choice
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
MAX_SIZE_TRAIN: 1333
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1333
FORMAT: "RGB" # "BGR" |