Backup / faster_rcnn.yaml
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MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
WEIGHTS: "https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl"
MASK_ON: False # Not doing segmentation
RESNETS:
OUT_FEATURES: ["res2", "res3", "res4", "res5"]
DEPTH: 50 # ResNet50
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: 2 # Change to suit own task
# Can reduce this for lower memory/faster training; Default 512
BATCH_SIZE_PER_IMAGE: 512
ROI_BOX_HEAD:
NAME: "FastRCNNConvFCHead"
NUM_FC: 2
POOLER_RESOLUTION: 7
ROI_MASK_HEAD:
NAME: "MaskRCNNConvUpsampleHead"
NUM_CONV: 4
POOLER_RESOLUTION: 14
BACKBONE:
NAME: "build_resnet_fpn_backbone"
FREEZE_AT: 2 # Default 2
DATASETS:
TRAIN: ("benign_train",)
TEST: ("benign_test",)
DATALOADER:
NUM_WORKERS: 0
SOLVER:
IMS_PER_BATCH: 12 # Batch size; Default 16
BASE_LR: 0.001
# (2/3, 8/9)
STEPS: (17000, 22000) # The iteration number to decrease learning rate by GAMMA.
MAX_ITER: 25000 # Number of training iterations
CHECKPOINT_PERIOD: 2500 # Saves checkpoint every number of steps
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) # Image input sizes
TEST:
# The period (in terms of steps) to evaluate the model during training.
# Set to 0 to disable.
EVAL_PERIOD: 2500
OUTPUT_DIR: "./output" # Specify output directory
VERSION: 2