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- architecture: RetinaNet
- max_iters: 90000
- use_gpu: true
- pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar
- weights: output/retinanet_r101_fpn_1x/model_final
- log_iter: 20
- snapshot_iter: 10000
- metric: COCO
- save_dir: output
- num_classes: 81
- RetinaNet:
- backbone: ResNet
- fpn: FPN
- retina_head: RetinaHead
- ResNet:
- norm_type: affine_channel
- norm_decay: 0.
- depth: 101
- feature_maps: [3, 4, 5]
- freeze_at: 2
- FPN:
- max_level: 7
- min_level: 3
- num_chan: 256
- spatial_scale: [0.03125, 0.0625, 0.125]
- has_extra_convs: true
- RetinaHead:
- num_convs_per_octave: 4
- num_chan: 256
- max_level: 7
- min_level: 3
- prior_prob: 0.01
- base_scale: 4
- num_scales_per_octave: 3
- anchor_generator:
- aspect_ratios: [1.0, 2.0, 0.5]
- variance: [1.0, 1.0, 1.0, 1.0]
- target_assign:
- positive_overlap: 0.5
- negative_overlap: 0.4
- gamma: 2.0
- alpha: 0.25
- sigma: 3.0151134457776365
- output_decoder:
- score_thresh: 0.05
- nms_thresh: 0.5
- pre_nms_top_n: 1000
- detections_per_im: 100
- nms_eta: 1.0
- LearningRate:
- base_lr: 0.01
- schedulers:
- - !PiecewiseDecay
- gamma: 0.1
- milestones: [60000, 80000]
- - !LinearWarmup
- start_factor: 0.3333333333333333
- steps: 500
- OptimizerBuilder:
- optimizer:
- momentum: 0.9
- type: Momentum
- regularizer:
- factor: 0.0001
- type: L2
- _READER_: 'faster_fpn_reader.yml'
- TrainReader:
- batch_size: 2
- batch_transforms:
- - !PadBatch
- pad_to_stride: 128
- EvalReader:
- batch_size: 2
- batch_transforms:
- - !PadBatch
- pad_to_stride: 128
- TestReader:
- batch_size: 1
- batch_transforms:
- - !PadBatch
- pad_to_stride: 128
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