Wheat_HEAD_Detection_Counting_ComputerVision_Model
/
detectron2
/configs
/COCO-InstanceSegmentation
/mask_rcnn_regnetx_4gf_dds_fpn_1x.py
from ..common.optim import SGD as optimizer | |
from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier | |
from ..common.data.coco import dataloader | |
from ..common.models.mask_rcnn_fpn import model | |
from ..common.train import train | |
from detectron2.config import LazyCall as L | |
from detectron2.modeling.backbone import RegNet | |
from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock | |
# Replace default ResNet with RegNetX-4GF from the DDS paper. Config source: | |
# https://github.com/facebookresearch/pycls/blob/2c152a6e5d913e898cca4f0a758f41e6b976714d/configs/dds_baselines/regnetx/RegNetX-4.0GF_dds_8gpu.yaml#L4-L9 # noqa | |
model.backbone.bottom_up = L(RegNet)( | |
stem_class=SimpleStem, | |
stem_width=32, | |
block_class=ResBottleneckBlock, | |
depth=23, | |
w_a=38.65, | |
w_0=96, | |
w_m=2.43, | |
group_width=40, | |
freeze_at=2, | |
norm="FrozenBN", | |
out_features=["s1", "s2", "s3", "s4"], | |
) | |
model.pixel_std = [57.375, 57.120, 58.395] | |
optimizer.weight_decay = 5e-5 | |
train.init_checkpoint = ( | |
"https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906383/RegNetX-4.0GF_dds_8gpu.pyth" | |
) | |
# RegNets benefit from enabling cudnn benchmark mode | |
train.cudnn_benchmark = True | |