e2e_mask_rcnn_R-50-C4_2x.yaml

MODEL:
  TYPE: generalized_rcnn
  CONV_BODY: ResNet.ResNet50_conv4_body
  FASTER_RCNN: True
  MASK_ON: True
RESNETS:
  IMAGENET_PRETRAINED_WEIGHTS: 'data/pretrained_model/resnet50_caffe.pth'
NUM_GPUS: 8
SOLVER:
  WEIGHT_DECAY: 0.0001
  LR_POLICY: steps_with_decay
  BASE_LR: 0.01
  GAMMA: 0.1
  # 2x schedule (note TRAIN.IMS_PER_BATCH: 1)
  MAX_ITER: 360000
  STEPS: [0, 240000, 320000]
RPN:
  SIZES: (32, 64, 128, 256, 512)
FAST_RCNN:
  ROI_BOX_HEAD: ResNet.ResNet_roi_conv5_head
  ROI_XFORM_METHOD: RoIAlign
MRCNN:
  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v0upshare
  RESOLUTION: 14
  ROI_XFORM_METHOD: RoIAlign
  ROI_XFORM_RESOLUTION: 14
  DILATION: 1  # default 2
  CONV_INIT: MSRAFill  # default: GaussianFill
TRAIN:
  SCALES: (800,)
  MAX_SIZE: 1333
  IMS_PER_BATCH: 1
  BATCH_SIZE_PER_IM: 512
TEST:
  SCALE: 800
  MAX_SIZE: 1333
  NMS: 0.5
  RPN_PRE_NMS_TOP_N: 6000
  RPN_POST_NMS_TOP_N: 1000