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nanodet-plus-m_MobileNetV2_14_320X192_DSM_Dataset_class4_20220211_fukang.yml
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# nanodet-plus-m_320
# COCO mAP(0.5:0.95) = 0.270
# AP_50 = 0.418
# AP_75 = 0.281
# AP_small = 0.083
# AP_m = 0.278
# AP_l = 0.451
save_dir: workspace/nanodet-plus-m_MobileNetV2_14_64_dconv_320X192_SGD500_DSM_Dataset_class4_20220211_fukang
model:
weight_averager:
name: ExpMovingAverager
decay: 0.9998
arch:
name: NanoDetPlus
detach_epoch: 10
backbone:
name: MobileNetV2
out_stages: [1, 4]
activation: LeakyReLU
fpn:
name: GhostPAN
in_channels: [24, 96]
out_channels: 64
kernel_size: 5
num_extra_level: 1
use_depthwise: True
activation: LeakyReLU
head:
name: NanoDetPlusHead
num_classes: 4
input_channel: 64
feat_channels: 64
stacked_convs: 2
kernel_size: 5
strides: [4, 16, 64]
activation: LeakyReLU
reg_max: 7
norm_cfg:
type: BN
loss:
loss_qfl:
name: QualityFocalLoss
use_sigmoid: True
beta: 2.0
loss_weight: 1.0
loss_dfl:
name: DistributionFocalLoss
loss_weight: 0.25
loss_bbox:
name: GIoULoss
loss_weight: 2.0
# Auxiliary head, only use in training time.
aux_head:
name: SimpleConvHead
num_classes: 4
input_channel: 128
feat_channels: 128
stacked_convs: 4
strides: [4, 16, 64]
activation: LeakyReLU
reg_max: 7
data:
train:
name: CocoDataset
img_path: /home/chenpengfei/dataset/DSM_Dataset_class4_20220211_fukang/train/image
ann_path: /home/chenpengfei/dataset/DSM_Dataset_class4_20220211_fukang/train/train.json
input_size: [320,192] #[w,h]
keep_ratio: False
load_mosaic: 0.0 # 增加mosaic数据增强
cut_mosaic: 0.0 # 变形版mosaic
pipeline:
perspective: 0.0 # 透视or仿射变换
scale: [0.6, 1.4] # 放缩
stretch: [[0.8, 1.2], [0.8, 1.2]] # 拉伸
rotation: 0 # 旋转
shear: 0 # 裁剪
translate: 0.2 # 平移
flip: 0.5 # 左右翻转
brightness: 0.2 # 亮度
contrast: [0.6, 1.4] # 对比度
saturation: [0.5, 1.2] # 饱和度
# normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]]
normalize: [[72.0, 72.0, 72.0], [64.0, 64.0, 64.0]]
val:
name: CocoDataset
img_path: /home/chenpengfei/dataset/DSM_Dataset_class4_20220211_fukang/val/image
ann_path: /home/chenpengfei/dataset/DSM_Dataset_class4_20220211_fukang/val/val.json
input_size: [320,192] #[w,h]
keep_ratio: False
pipeline:
# normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]]
normalize: [[72.0, 72.0, 72.0], [64.0, 64.0, 64.0]]
device:
gpu_ids: [1,2,3,4,5,6] # Set like [0, 1, 2, 3] if you have multi-GPUs
workers_per_gpu: 10
batchsize_per_gpu: 128
schedule:
# resume: workspace/nanodet-plus-m_320X192_DSMhand_smoke3_MultiStepLR_280/model_last.ckpt
# load_model:
# optimizer:
# name: AdamW
# lr: 0.001
# weight_decay: 0.05
# warmup:
# name: linear
# steps: 500
# ratio: 0.0001
# total_epochs: 300
# lr_schedule:
# name: CosineAnnealingLR
# T_max: 300
# eta_min: 0.00005
# val_intervals: 10
optimizer:
name: SGD
lr: 0.14
momentum: 0.9
weight_decay: 0.0001
warmup:
name: linear
steps: 400
ratio: 0.1
total_epochs: 500
lr_schedule:
name: MultiStepLR
milestones: [ 300,400,450 ]
gamma: 0.1
val_intervals: 10
grad_clip: 35
evaluator:
name: CocoDetectionEvaluator
save_key: mAP
log:
interval: 50
class_names: ['face', 'hand', 'cigarette', 'cellphone']