fuliucansheng
/

detectron2

Model card Files Files and versions Community

detectron2 / dyhead /faster_rcnn_r50_config.yaml

fuliucansheng's picture

add dyhead configs & weights

506cc79 almost 2 years ago

raw history blame contribute delete

No virus

1.39 kB

	MODEL:
	META_ARCHITECTURE: "GeneralizedRCNN"
	WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
	BACKBONE:
	NAME: "build_resnet_fpn_dyhead_backbone"
	RESNETS:
	DEPTH: 50
	OUT_FEATURES: ["res2", "res3", "res4", "res5"]
	FPN:
	IN_FEATURES: ["res2", "res3", "res4", "res5"]
	DYHEAD:
	NUM_CONVS: 6
	CHANNELS: 256
	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"]
	ROI_BOX_HEAD:
	NAME: "FastRCNNConvFCHead"
	NUM_FC: 2
	POOLER_RESOLUTION: 7
	ROI_MASK_HEAD:
	NAME: "MaskRCNNConvUpsampleHead"
	NUM_CONV: 4
	POOLER_RESOLUTION: 14
	DATASETS:
	TRAIN: ("coco_2017_train",)
	TEST: ("coco_2017_val",)
	SOLVER:
	IMS_PER_BATCH: 16
	BASE_LR: 0.02
	STEPS: (60000, 80000)
	MAX_ITER: 90000
	VERSION: 2