/
mask_rcnn_R50FPN_CLIP_sigmoid.yaml
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mask_rcnn_R50FPN_CLIP_sigmoid.yaml
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MODEL:
META_ARCHITECTURE: "CustomRCNN"
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
BACKBONE:
NAME: "build_resnet_fpn_backbone"
RESNETS:
OUT_FEATURES: [ "res2", "res3", "res4", "res5" ]
DEPTH: 50
FPN:
IN_FEATURES: [ "res2", "res3", "res4", "res5" ]
ANCHOR_GENERATOR:
SIZES: [ [ 32 ], [ 64 ], [ 128 ], [ 256 ], [ 512 ] ]
ASPECT_RATIOS: [ [ 0.5, 1.0, 2.0 ] ]
RPN:
IN_FEATURES: [ "p2", "p3", "p4", "p5", "p6" ]
PRE_NMS_TOPK_TRAIN: 2000
PRE_NMS_TOPK_TEST: 1000
POST_NMS_TOPK_TRAIN: 1000
POST_NMS_TOPK_TEST: 1000
ROI_HEADS:
NAME: "CustomStandardROIHeads"
IN_FEATURES: [ "p2", "p3", "p4", "p5" ]
NUM_CLASSES: 1203
SCORE_THRESH_TEST: 0.0001
ROI_BOX_HEAD:
NAME: "FastRCNNConvFCHead"
NUM_FC: 2
POOLER_RESOLUTION: 7
CLS_AGNOSTIC_BBOX_REG: True
USE_ZEROSHOT_CLS: True
ZEROSHOT_WEIGHT_PATH: 'datasets/zeroshot_weights/lvis_v1_clip_a+photo+cname.npy'
CAT_FREQ_PATH: 'datasets/lvis/lvis_v1_train_norare_cat_info.json'
USE_SIGMOID_CE: True
USE_FED_LOSS: True
IGNORE_ZERO_CATS: True
USE_BIAS: -4.6
ROI_MASK_HEAD:
NAME: "MaskRCNNConvUpsampleHead"
CLS_AGNOSTIC_MASK: True
NUM_CONV: 4
POOLER_RESOLUTION: 14
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
MAX_SIZE_TRAIN: 1333
DATASETS:
TRAIN: ("lvis_v1_train_norare",)
TEST: ("lvis_v1_val",)
TEST:
DETECTIONS_PER_IMAGE: 300
SOLVER:
IMS_PER_BATCH: 16 # Overall batch-size
BASE_LR: 0.02
STEPS: (60000, 80000)
MAX_ITER: 90000
WARMUP_FACTOR: 0.001
WARMUP_ITERS: 1000
DATALOADER:
SAMPLER_TRAIN: "RepeatFactorTrainingSampler"
REPEAT_THRESHOLD: 0.001
VERSION: 2
OUTPUT_DIR: output/lvis_ovd_base