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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 | |