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# ----------------------------------------------------------------------------
# Train a Faster R-CNN with ResNet-50 and C4 backbone. This config follows
# Detectron2 format; and is unrelated with our VirTex configs. Params here
# replicate evaluation protocol as per MoCo (https://arxiv.org/abs/1911.05722).
# ----------------------------------------------------------------------------
INPUT:
# Input format will always be RGB, consistent with torchvision.
FORMAT: "RGB"
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
MIN_SIZE_TEST: 800
MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
# Train all layers end-to-end by default.
BACKBONE:
NAME: build_resnet_backbone
FREEZE_AT: 0
# Fine-tune with SyncBN.
# STRIDE_IN_1X1 is False for torchvision-like models.
RESNETS:
DEPTH: 50
NORM: SyncBN
STRIDE_IN_1X1: False
RPN:
PRE_NMS_TOPK_TEST: 6000
POST_NMS_TOPK_TEST: 1000
# ROI head with extra BN layer after res5 stage.
ROI_HEADS:
NAME: "Res5ROIHeadsExtraNorm"
# ImageNet color mean for torchvision-like models (RGB order).
PIXEL_MEAN: [123.675, 116.280, 103.530]
PIXEL_STD: [58.395, 57.120, 57.375]
SOLVER:
# This is for 8 GPUs, apply linear scaling for 4 GPUs.
IMS_PER_BATCH: 16
BASE_LR: 0.02
TEST:
PRECISE_BN:
ENABLED: True
VERSION: 2
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