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MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
BACKBONE:
NAME: "build_resnet_fpn_backbone"
RESNETS:
OUT_FEATURES: ["res2", "res3", "res4", "res5"]
FPN:
IN_FEATURES: ["res2", "res3", "res4", "res5"]
ANCHOR_GENERATOR:
SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map
ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps)
RPN:
IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level
PRE_NMS_TOPK_TEST: 1000 # Per FPN level
# Detectron1 uses 2000 proposals per-batch,
# (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
# which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
POST_NMS_TOPK_TRAIN: 1000
POST_NMS_TOPK_TEST: 1000
ROI_HEADS:
NAME: "StandardROIHeads"
IN_FEATURES: ["p2", "p3", "p4", "p5"]
NUM_CLASSES: 1
ROI_BOX_HEAD:
NAME: "FastRCNNConvFCHead"
NUM_FC: 2
POOLER_RESOLUTION: 7
ROI_MASK_HEAD:
NAME: "MaskRCNNConvUpsampleHead"
NUM_CONV: 4
POOLER_RESOLUTION: 14
DATASETS:
TRAIN: ("base_coco_2017_train", "densepose_coco_2014_train")
TEST: ("densepose_chimps",)
CATEGORY_MAPS:
"base_coco_2017_train":
"16": 1 # bird -> person
"17": 1 # cat -> person
"18": 1 # dog -> person
"19": 1 # horse -> person
"20": 1 # sheep -> person
"21": 1 # cow -> person
"22": 1 # elephant -> person
"23": 1 # bear -> person
"24": 1 # zebra -> person
"25": 1 # girafe -> person
"base_coco_2017_val":
"16": 1 # bird -> person
"17": 1 # cat -> person
"18": 1 # dog -> person
"19": 1 # horse -> person
"20": 1 # sheep -> person
"21": 1 # cow -> person
"22": 1 # elephant -> person
"23": 1 # bear -> person
"24": 1 # zebra -> person
"25": 1 # girafe -> person
WHITELISTED_CATEGORIES:
"base_coco_2017_train":
- 1 # person
- 16 # bird
- 17 # cat
- 18 # dog
- 19 # horse
- 20 # sheep
- 21 # cow
- 22 # elephant
- 23 # bear
- 24 # zebra
- 25 # girafe
"base_coco_2017_val":
- 1 # person
- 16 # bird
- 17 # cat
- 18 # dog
- 19 # horse
- 20 # sheep
- 21 # cow
- 22 # elephant
- 23 # bear
- 24 # zebra
- 25 # girafe
SOLVER:
IMS_PER_BATCH: 16
BASE_LR: 0.02
STEPS: (60000, 80000)
MAX_ITER: 90000
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
VERSION: 2
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