未验证 提交 1a7d2209 编写于 作者: T Tingquan Gao 提交者: gaotingquan

add safety helmet config

上级 b457c393
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output
device: gpu
save_interval: 1
eval_during_train: True
start_eval_epoch: 1
eval_interval: 1
epochs: 40
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: ./inference
# training model under @to_static
to_static: False
use_dali: False
# model architecture
Arch:
name: "DistillationModel"
class_num: &class_num 2
# if not null, its lengths should be same as models
pretrained_list:
# if not null, its lengths should be same as models
freeze_params_list:
- True
- False
use_sync_bn: True
models:
- Teacher:
name: ResNet101_vd
class_num: *class_num
pretrained: pretrained/teacher_ResNet101_vd_0/ResNet101_vd/best_model
- Student:
name: PPLCNet_x1_0
class_num: *class_num
pretrained: True
use_ssld: True
infer_model_name: "Student"
# loss function config for traing/eval process
Loss:
Train:
- DistillationDMLLoss:
weight: 1.0
model_name_pairs:
- ["Student", "Teacher"]
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Cosine
learning_rate: 0.015
warmup_epoch: 5
regularizer:
name: 'L2'
coeff: 0.00003
# data loader for train and eval
DataLoader:
Train:
dataset:
name: ImageNetDataset
image_root: ./dataset/SafetyHelmetV3/
cls_label_path: ./dataset/SafetyHelmetV3/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- RandCropImage:
size: 192
- RandFlipImage:
flip_code: 1
- TimmAutoAugment:
prob: 0.5
config_str: rand-m9-mstd0.5-inc1
interpolation: bicubic
img_size: 192
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- RandomErasing:
EPSILON: 0.5
sl: 0.02
sh: 1.0/3.0
r1: 0.3
attempt: 10
use_log_aspect: True
mode: pixel
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: True
loader:
num_workers: 8
use_shared_memory: True
Eval:
dataset:
name: ImageNetDataset
image_root: ./dataset/SafetyHelmetV3/
cls_label_path: ./dataset/SafetyHelmetV3/eval_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 1
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
PostProcess:
name: ThreshOutput
threshold: 0.9235
label_0: wearing
label_1: unwearing
Metric:
Train:
- DistillationTopkAcc:
model_key: "Student"
topk: [1, 2]
Eval:
- TprAtFpr:
max_fpr: 0.0001
- TopkAcc:
topk: [1, 2]
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output_002_2/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 40
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: ./inference
# model architecture
Arch:
name: MobileNetV3_large_x1_0
pretrained: True
class_num: 2
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
epsilon: 0.1
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Cosine
learning_rate: 0.050
warmup_epoch: 5
regularizer:
name: 'L2'
coeff: 0.00002
# data loader for train and eval
DataLoader:
Train:
dataset:
name: ImageNetDataset
image_root: ./dataset/SafetyHelmetV3/
cls_label_path: ./dataset/SafetyHelmetV3/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- RandCropImage:
size: 224
- RandFlipImage:
flip_code: 1
- AutoAugment:
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 256
drop_last: False
shuffle: True
loader:
num_workers: 8
use_shared_memory: True
Eval:
dataset:
name: ImageNetDataset
image_root: ./dataset/SafetyHelmetV3/
cls_label_path: ./dataset/SafetyHelmetV3/eval_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 1
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
PostProcess:
name: ThreshOutput
threshold: 0.9235
label_0: wearing
label_1: unwearing
Metric:
Train:
- TopkAcc:
topk: [1, 2]
Eval:
- TprAtFpr:
max_fpr: 0.0001
- TopkAcc:
topk: [1, 2]
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output_003_4/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 40
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: ./inference
# model architecture
Arch:
name: SwinTransformer_tiny_patch4_window7_224
pretrained: True
class_num: 2
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
epsilon: 0.1
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: AdamW
beta1: 0.9
beta2: 0.999
epsilon: 1e-8
weight_decay: 0.05
no_weight_decay_name: absolute_pos_embed relative_position_bias_table .bias norm
one_dim_param_no_weight_decay: True
lr:
name: Cosine
learning_rate: 1e-5
eta_min: 1e-7
warmup_epoch: 5
warmup_start_lr: 1e-6
# data loader for train and eval
DataLoader:
Train:
dataset:
name: ImageNetDataset
image_root: ./dataset/SafetyHelmetV3/
cls_label_path: ./dataset/SafetyHelmetV3/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- RandCropImage:
size: 224
interpolation: bicubic
backend: pil
- RandFlipImage:
flip_code: 1
- TimmAutoAugment:
config_str: rand-m9-mstd0.5-inc1
interpolation: bicubic
img_size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- RandomErasing:
EPSILON: 0.25
sl: 0.02
sh: 1.0/3.0
r1: 0.3
attempt: 10
use_log_aspect: True
mode: pixel
batch_transform_ops:
- OpSampler:
MixupOperator:
alpha: 0.8
prob: 0.5
CutmixOperator:
alpha: 1.0
prob: 0.5
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: True
loader:
num_workers: 8
use_shared_memory: True
Eval:
dataset:
name: ImageNetDataset
image_root: ./dataset/SafetyHelmetV3/
cls_label_path: ./dataset/SafetyHelmetV3/eval_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 256
interpolation: bicubic
backend: pil
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 128
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 1
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
PostProcess:
name: ThreshOutput
threshold: 0.9235
label_0: wearing
label_1: unwearing
Metric:
Eval:
- TprAtFpr:
max_fpr: 0.0001
- TopkAcc:
topk: [1, 2]
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output_001_5/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 40
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: ./inference
# model architecture
Arch:
name: PPLCNet_x1_0
pretrained: True
use_ssld: True
class_num: 2
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
epsilon: 0.1
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Cosine
learning_rate: 0.01
warmup_epoch: 5
regularizer:
name: 'L2'
coeff: 0.00003
# data loader for train and eval
DataLoader:
Train:
dataset:
name: ImageNetDataset
image_root: ./dataset/SafetyHelmetV3/
cls_label_path: ./dataset/SafetyHelmetV3/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- RandCropImage:
size: 224
- RandFlipImage:
flip_code: 1
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: True
loader:
num_workers: 8
use_shared_memory: True
Eval:
dataset:
name: ImageNetDataset
image_root: ./dataset/SafetyHelmetV3/
cls_label_path: ./dataset/SafetyHelmetV3/eval_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 1
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
PostProcess:
name: ThreshOutput
threshold: 0.9235
label_0: wearing
label_1: unwearing
Metric:
Train:
- TopkAcc:
topk: [1, 2]
Eval:
- TprAtFpr:
max_fpr: 0.0001
- TopkAcc:
topk: [1, 2]
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output_001_5/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 40
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: ./inference
# model architecture
Arch:
name: PPLCNet_x1_0
pretrained: True
use_ssld: True
class_num: 2
use_sync_bn: True
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
epsilon: 0.1
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Cosine
learning_rate: 0.015
warmup_epoch: 5
regularizer:
name: 'L2'
coeff: 0.00003
# data loader for train and eval
DataLoader:
Train:
dataset:
name: ImageNetDataset
image_root: ./dataset/SafetyHelmetV3/
cls_label_path: ./dataset/SafetyHelmetV3/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- RandCropImage:
size: 192
- RandFlipImage:
flip_code: 1
- TimmAutoAugment:
prob: 0.5
config_str: rand-m9-mstd0.5-inc1
interpolation: bicubic
img_size: 192
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- RandomErasing:
EPSILON: 0.5
sl: 0.02
sh: 1.0/3.0
r1: 0.3
attempt: 10
use_log_aspect: True
mode: pixel
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: True
loader:
num_workers: 8
use_shared_memory: True
Eval:
dataset:
name: ImageNetDataset
image_root: ./dataset/SafetyHelmetV3/
cls_label_path: ./dataset/SafetyHelmetV3/eval_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 1
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
PostProcess:
name: ThreshOutput
threshold: 0.9235
label_0: wearing
label_1: unwearing
Metric:
Train:
- TopkAcc:
topk: [1, 2]
Eval:
- TprAtFpr:
max_fpr: 0.0001
- TopkAcc:
topk: [1, 2]
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