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picodet_s_192_pedestrian.yml
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picodet_s_192_pedestrian.yml
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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_192_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: [128, 160, 192, 224, 256], 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: [192, 192], 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, 192, 192]
sample_transforms:
- Decode: {}
- Resize: {interp: 2, target_size: [192, 192], 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