forked from PaddlePaddle/PaddleDetection
-
Notifications
You must be signed in to change notification settings - Fork 0
/
picodet_s_320_pedestrian.yml
143 lines (130 loc) · 3.06 KB
/
picodet_s_320_pedestrian.yml
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
use_gpu: true
log_iter: 20
save_dir: output
snapshot_epoch: 1
print_flops: false
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ESNet_x0_75_pretrained.pdparams
weights: output/picodet_s_320_pedestrian/model_final
find_unused_parameters: True
use_ema: true
cycle_epoch: 40
snapshot_epoch: 10
epoch: 300
metric: COCO
num_classes: 1
architecture: PicoDet
PicoDet:
backbone: ESNet
neck: CSPPAN
head: PicoHead
ESNet:
scale: 0.75
feature_maps: [4, 11, 14]
act: hard_swish
channel_ratio: [0.875, 0.5, 0.5, 0.5, 0.625, 0.5, 0.625, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
CSPPAN:
out_channels: 96
use_depthwise: True
num_csp_blocks: 1
num_features: 4
PicoHead:
conv_feat:
name: PicoFeat
feat_in: 96
feat_out: 96
num_convs: 2
num_fpn_stride: 4
norm_type: bn
share_cls_reg: True
fpn_stride: [8, 16, 32, 64]
feat_in_chan: 96
prior_prob: 0.01
reg_max: 7
cell_offset: 0.5
loss_class:
name: VarifocalLoss
use_sigmoid: True
iou_weighted: True
loss_weight: 1.0
loss_dfl:
name: DistributionFocalLoss
loss_weight: 0.25
loss_bbox:
name: GIoULoss
loss_weight: 2.0
assigner:
name: SimOTAAssigner
candidate_topk: 10
iou_weight: 6
nms:
name: MultiClassNMS
nms_top_k: 1000
keep_top_k: 100
score_threshold: 0.025
nms_threshold: 0.6
LearningRate:
base_lr: 0.4
schedulers:
- !CosineDecay
max_epochs: 300
- !LinearWarmup
start_factor: 0.1
steps: 300
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.00004
type: L2
TrainDataset:
!COCODataSet
image_dir: ""
anno_path: aic_coco_train_cocoformat.json
dataset_dir: dataset
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
EvalDataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco
TestDataset:
!ImageFolder
anno_path: annotations/instances_val2017.json
worker_num: 8
TrainReader:
sample_transforms:
- Decode: {}
- RandomCrop: {}
- RandomFlip: {prob: 0.5}
- RandomDistort: {}
batch_transforms:
- BatchRandomResize: {target_size: [256, 288, 320, 352, 384], random_size: True, random_interp: True, keep_ratio: False}
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
- Permute: {}
batch_size: 128
shuffle: true
drop_last: true
collate_batch: false
EvalReader:
sample_transforms:
- Decode: {}
- Resize: {interp: 2, target_size: [320, 320], keep_ratio: False}
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
- Permute: {}
batch_transforms:
- PadBatch: {pad_to_stride: 32}
batch_size: 8
shuffle: false
TestReader:
inputs_def:
image_shape: [1, 3, 320, 320]
sample_transforms:
- Decode: {}
- Resize: {interp: 2, target_size: [320, 320], keep_ratio: False}
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
- Permute: {}
batch_transforms:
- PadBatch: {pad_to_stride: 32}
batch_size: 1
shuffle: false