更换文档检测模型

This commit is contained in:
2024-08-27 14:42:45 +08:00
parent aea6f19951
commit 1514e09c40
2072 changed files with 254336 additions and 4967 deletions

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epoch: 405
LearningRate:
base_lr: 0.01
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones:
- 243
- 324
- !LinearWarmup
start_factor: 0.
steps: 4000
OptimizerBuilder:
clip_grad_by_norm: 35.
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0005
type: L2

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epoch: 811
LearningRate:
base_lr: 0.01
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones:
- 649
- 730
- !LinearWarmup
start_factor: 0.
steps: 4000
OptimizerBuilder:
clip_grad_by_norm: 35.
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0005
type: L2

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epoch: 365
LearningRate:
base_lr: 0.005
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones:
- 243
- !LinearWarmup
start_factor: 0.
steps: 4000
OptimizerBuilder:
clip_grad_by_norm: 35.
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0005
type: L2

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epoch: 650
LearningRate:
base_lr: 0.005
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones:
- 430
- 540
- 610
- !LinearWarmup
start_factor: 0.
steps: 4000
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0005
type: L2

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architecture: YOLOv3
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/MobileNetV3_large_x1_0_ssld_pretrained.pdparams
norm_type: sync_bn
use_ema: true
ema_decay: 0.9998
YOLOv3:
backbone: MobileNetV3
neck: PPYOLOFPN
yolo_head: YOLOv3Head
post_process: BBoxPostProcess
MobileNetV3:
model_name: large
scale: 1.
with_extra_blocks: false
extra_block_filters: []
feature_maps: [13, 16]
PPYOLOFPN:
in_channels: [160, 368]
coord_conv: true
conv_block_num: 0
spp: true
drop_block: true
YOLOv3Head:
anchors: [[11, 18], [34, 47], [51, 126],
[115, 71], [120, 195], [254, 235]]
anchor_masks: [[3, 4, 5], [0, 1, 2]]
loss: YOLOv3Loss
YOLOv3Loss:
ignore_thresh: 0.5
downsample: [32, 16]
label_smooth: false
scale_x_y: 1.05
iou_loss: IouLoss
IouLoss:
loss_weight: 2.5
loss_square: true
BBoxPostProcess:
decode:
name: YOLOBox
conf_thresh: 0.005
downsample_ratio: 32
clip_bbox: true
scale_x_y: 1.05
nms:
name: MultiClassNMS
keep_top_k: 100
nms_threshold: 0.45
nms_top_k: 1000
score_threshold: 0.005

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architecture: YOLOv3
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/MobileNetV3_small_x1_0_ssld_pretrained.pdparams
norm_type: sync_bn
use_ema: true
ema_decay: 0.9998
YOLOv3:
backbone: MobileNetV3
neck: PPYOLOFPN
yolo_head: YOLOv3Head
post_process: BBoxPostProcess
MobileNetV3:
model_name: small
scale: 1.
with_extra_blocks: false
extra_block_filters: []
feature_maps: [9, 12]
PPYOLOFPN:
in_channels: [96, 304]
coord_conv: true
conv_block_num: 0
spp: true
drop_block: true
YOLOv3Head:
anchors: [[11, 18], [34, 47], [51, 126],
[115, 71], [120, 195], [254, 235]]
anchor_masks: [[3, 4, 5], [0, 1, 2]]
loss: YOLOv3Loss
YOLOv3Loss:
ignore_thresh: 0.5
downsample: [32, 16]
label_smooth: false
scale_x_y: 1.05
iou_loss: IouLoss
IouLoss:
loss_weight: 2.5
loss_square: true
BBoxPostProcess:
decode:
name: YOLOBox
conf_thresh: 0.005
downsample_ratio: 32
clip_bbox: true
scale_x_y: 1.05
nms:
name: MultiClassNMS
keep_top_k: 100
nms_threshold: 0.45
nms_top_k: 1000
score_threshold: 0.005

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architecture: YOLOv3
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet18_vd_pretrained.pdparams
norm_type: sync_bn
use_ema: true
ema_decay: 0.9998
YOLOv3:
backbone: ResNet
neck: PPYOLOFPN
yolo_head: YOLOv3Head
post_process: BBoxPostProcess
ResNet:
depth: 18
variant: d
return_idx: [2, 3]
freeze_at: -1
freeze_norm: false
norm_decay: 0.
PPYOLOFPN:
drop_block: true
block_size: 3
keep_prob: 0.9
conv_block_num: 0
YOLOv3Head:
anchor_masks: [[3, 4, 5], [0, 1, 2]]
anchors: [[10, 14], [23, 27], [37, 58],
[81, 82], [135, 169], [344, 319]]
loss: YOLOv3Loss
YOLOv3Loss:
ignore_thresh: 0.7
downsample: [32, 16]
label_smooth: false
scale_x_y: 1.05
iou_loss: IouLoss
IouLoss:
loss_weight: 2.5
loss_square: true
BBoxPostProcess:
decode:
name: YOLOBox
conf_thresh: 0.01
downsample_ratio: 32
clip_bbox: true
scale_x_y: 1.05
nms:
name: MatrixNMS
keep_top_k: 100
score_threshold: 0.01
post_threshold: 0.01
nms_top_k: -1
background_label: -1

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architecture: YOLOv3
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_pretrained.pdparams
norm_type: sync_bn
use_ema: true
ema_decay: 0.9998
YOLOv3:
backbone: ResNet
neck: PPYOLOFPN
yolo_head: YOLOv3Head
post_process: BBoxPostProcess
ResNet:
depth: 50
variant: d
return_idx: [1, 2, 3]
dcn_v2_stages: [3]
freeze_at: -1
freeze_norm: false
norm_decay: 0.
PPYOLOFPN:
coord_conv: true
drop_block: true
block_size: 3
keep_prob: 0.9
spp: true
YOLOv3Head:
anchors: [[10, 13], [16, 30], [33, 23],
[30, 61], [62, 45], [59, 119],
[116, 90], [156, 198], [373, 326]]
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
loss: YOLOv3Loss
iou_aware: true
iou_aware_factor: 0.4
YOLOv3Loss:
ignore_thresh: 0.7
downsample: [32, 16, 8]
label_smooth: false
scale_x_y: 1.05
iou_loss: IouLoss
iou_aware_loss: IouAwareLoss
IouLoss:
loss_weight: 2.5
loss_square: true
IouAwareLoss:
loss_weight: 1.0
BBoxPostProcess:
decode:
name: YOLOBox
conf_thresh: 0.01
downsample_ratio: 32
clip_bbox: true
scale_x_y: 1.05
nms:
name: MatrixNMS
keep_top_k: 100
score_threshold: 0.01
post_threshold: 0.01
nms_top_k: -1
background_label: -1

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worker_num: 2
TrainReader:
inputs_def:
num_max_boxes: 50
sample_transforms:
- Decode: {}
- Mixup: {alpha: 1.5, beta: 1.5}
- RandomDistort: {}
- RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
- RandomCrop: {}
- RandomFlip: {}
batch_transforms:
- BatchRandomResize: {target_size: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608], random_size: True, random_interp: True, keep_ratio: False}
- NormalizeBox: {}
- PadBox: {num_max_boxes: 50}
- BboxXYXY2XYWH: {}
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- Permute: {}
- Gt2YoloTarget: {anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]], anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]], downsample_ratios: [32, 16, 8]}
batch_size: 24
shuffle: true
drop_last: true
mixup_epoch: 25000
use_shared_memory: true
EvalReader:
sample_transforms:
- Decode: {}
- Resize: {target_size: [608, 608], keep_ratio: False, interp: 2}
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- Permute: {}
batch_size: 8
TestReader:
inputs_def:
image_shape: [3, 608, 608]
sample_transforms:
- Decode: {}
- Resize: {target_size: [608, 608], keep_ratio: False, interp: 2}
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- Permute: {}
batch_size: 1

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architecture: YOLOv3
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/MobileNetV3_large_x0_5_pretrained.pdparams
norm_type: sync_bn
use_ema: true
ema_decay: 0.9998
YOLOv3:
backbone: MobileNetV3
neck: PPYOLOTinyFPN
yolo_head: YOLOv3Head
post_process: BBoxPostProcess
MobileNetV3:
model_name: large
scale: .5
with_extra_blocks: false
extra_block_filters: []
feature_maps: [7, 13, 16]
PPYOLOTinyFPN:
detection_block_channels: [160, 128, 96]
spp: true
drop_block: true
YOLOv3Head:
anchors: [[10, 15], [24, 36], [72, 42],
[35, 87], [102, 96], [60, 170],
[220, 125], [128, 222], [264, 266]]
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
loss: YOLOv3Loss
YOLOv3Loss:
ignore_thresh: 0.5
downsample: [32, 16, 8]
label_smooth: false
scale_x_y: 1.05
iou_loss: IouLoss
IouLoss:
loss_weight: 2.5
loss_square: true
BBoxPostProcess:
decode:
name: YOLOBox
conf_thresh: 0.005
downsample_ratio: 32
clip_bbox: true
scale_x_y: 1.05
nms:
name: MultiClassNMS
keep_top_k: 100
nms_threshold: 0.45
nms_top_k: 1000
score_threshold: 0.005

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worker_num: 4
TrainReader:
inputs_def:
num_max_boxes: 100
sample_transforms:
- Decode: {}
- Mixup: {alpha: 1.5, beta: 1.5}
- RandomDistort: {}
- RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
- RandomCrop: {}
- RandomFlip: {}
batch_transforms:
- BatchRandomResize: {target_size: [192, 224, 256, 288, 320, 352, 384, 416, 448, 480, 512], random_size: True, random_interp: True, keep_ratio: False}
- NormalizeBox: {}
- PadBox: {num_max_boxes: 100}
- BboxXYXY2XYWH: {}
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- Permute: {}
- Gt2YoloTarget: {anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]], anchors: [[10, 15], [24, 36], [72, 42], [35, 87], [102, 96], [60, 170], [220, 125], [128, 222], [264, 266]], downsample_ratios: [32, 16, 8]}
batch_size: 32
shuffle: true
drop_last: true
mixup_epoch: 500
use_shared_memory: true
EvalReader:
sample_transforms:
- Decode: {}
- Resize: {target_size: [320, 320], keep_ratio: False, interp: 2}
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- Permute: {}
batch_size: 8
TestReader:
inputs_def:
image_shape: [3, 320, 320]
sample_transforms:
- Decode: {}
- Resize: {target_size: [320, 320], keep_ratio: False, interp: 2}
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- Permute: {}
batch_size: 1

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architecture: YOLOv3
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_pretrained.pdparams
norm_type: sync_bn
use_ema: true
ema_decay: 0.9998
# AMP training
master_grad: true
YOLOv3:
backbone: ResNet
neck: PPYOLOPAN
yolo_head: YOLOv3Head
post_process: BBoxPostProcess
ResNet:
depth: 50
variant: d
return_idx: [1, 2, 3]
dcn_v2_stages: [3]
freeze_at: -1
freeze_norm: false
norm_decay: 0.
PPYOLOPAN:
drop_block: true
block_size: 3
keep_prob: 0.9
spp: true
YOLOv3Head:
anchors: [[10, 13], [16, 30], [33, 23],
[30, 61], [62, 45], [59, 119],
[116, 90], [156, 198], [373, 326]]
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
loss: YOLOv3Loss
iou_aware: true
iou_aware_factor: 0.5
YOLOv3Loss:
ignore_thresh: 0.7
downsample: [32, 16, 8]
label_smooth: false
scale_x_y: 1.05
iou_loss: IouLoss
iou_aware_loss: IouAwareLoss
IouLoss:
loss_weight: 2.5
loss_square: true
IouAwareLoss:
loss_weight: 1.0
BBoxPostProcess:
decode:
name: YOLOBox
conf_thresh: 0.01
downsample_ratio: 32
clip_bbox: true
scale_x_y: 1.05
nms:
name: MatrixNMS
keep_top_k: 100
score_threshold: 0.01
post_threshold: 0.01
nms_top_k: -1
background_label: -1

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worker_num: 2
TrainReader:
inputs_def:
num_max_boxes: 100
sample_transforms:
- Decode: {}
- Mixup: {alpha: 1.5, beta: 1.5}
- RandomDistort: {}
- RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
- RandomCrop: {}
- RandomFlip: {}
batch_transforms:
- BatchRandomResize: {target_size: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608, 640, 672, 704, 736, 768], random_size: True, random_interp: True, keep_ratio: False}
- NormalizeBox: {}
- PadBox: {num_max_boxes: 100}
- BboxXYXY2XYWH: {}
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- Permute: {}
- Gt2YoloTarget: {anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]], anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]], downsample_ratios: [32, 16, 8]}
batch_size: 12
shuffle: true
drop_last: true
mixup_epoch: 25000
use_shared_memory: true
EvalReader:
sample_transforms:
- Decode: {}
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- Permute: {}
batch_size: 8
TestReader:
inputs_def:
image_shape: [3, 640, 640]
sample_transforms:
- Decode: {}
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- Permute: {}
batch_size: 1