forked from PulseFocusPlatform/PulseFocusPlatform
170 lines
3.7 KiB
YAML
170 lines
3.7 KiB
YAML
architecture: SSD
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use_gpu: true
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max_iters: 400000
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snapshot_iter: 20000
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log_iter: 20
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metric: COCO
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pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x1_0_ssld_pretrained.tar
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save_dir: output
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weights: output/ssdlite_mobilenet_v3_small_fpn/model_final
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# 80(label_class) + 1(background)
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num_classes: 81
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SSD:
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backbone: MobileNetV3
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fpn: FPN
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multi_box_head: SSDLiteMultiBoxHead
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output_decoder:
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background_label: 0
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keep_top_k: 200
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nms_eta: 1.0
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nms_threshold: 0.45
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nms_top_k: 400
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score_threshold: 0.01
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FPN:
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num_chan: 256
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max_level: 7
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norm_type: bn
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norm_decay: 0.00004
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reverse_out: true
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MobileNetV3:
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scale: 1.0
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model_name: small
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extra_block_filters: [[256, 512], [128, 256], [128, 256], [64, 128]]
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feature_maps: [5, 7, 8, 9, 10, 11]
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lr_mult_list: [0.25, 0.25, 0.5, 0.5, 0.75]
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conv_decay: 0.00004
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SSDLiteMultiBoxHead:
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aspect_ratios: [[2.], [2., 3.], [2., 3.], [2., 3.], [2., 3.], [2., 3.]]
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base_size: 320
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steps: [16, 32, 64, 107, 160, 320]
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flip: true
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clip: true
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max_ratio: 95
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min_ratio: 20
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offset: 0.5
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conv_decay: 0.00004
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LearningRate:
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base_lr: 0.4
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schedulers:
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- !CosineDecay
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max_iters: 400000
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- !LinearWarmup
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start_factor: 0.33333
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steps: 2000
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OptimizerBuilder:
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optimizer:
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momentum: 0.9
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type: Momentum
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regularizer:
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factor: 0.0005
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type: L2
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TrainReader:
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inputs_def:
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image_shape: [3, 320, 320]
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fields: ['image', 'gt_bbox', 'gt_class']
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dataset:
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!COCODataSet
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dataset_dir: dataset/coco
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anno_path: annotations/instances_train2017.json
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image_dir: train2017
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sample_transforms:
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- !DecodeImage
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to_rgb: true
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- !RandomDistort
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brightness_lower: 0.875
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brightness_upper: 1.125
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is_order: true
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- !RandomExpand
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fill_value: [123.675, 116.28, 103.53]
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- !RandomCrop
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allow_no_crop: false
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- !NormalizeBox {}
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- !ResizeImage
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interp: 1
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target_size: 320
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use_cv2: false
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- !RandomFlipImage
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is_normalized: false
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- !NormalizeImage
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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is_scale: true
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is_channel_first: false
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- !Permute
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to_bgr: false
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channel_first: true
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batch_size: 64
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shuffle: true
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drop_last: true
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# Number of working threads/processes. To speed up, can be set to 16 or 32 etc.
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worker_num: 8
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# Size of shared memory used in result queue. After increasing `worker_num`, need expand `memsize`.
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memsize: 8G
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# Buffer size for multi threads/processes.one instance in buffer is one batch data.
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# To speed up, can be set to 64 or 128 etc.
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bufsize: 32
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use_process: true
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EvalReader:
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inputs_def:
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image_shape: [3, 320, 320]
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fields: ['image', 'gt_bbox', 'gt_class', 'im_shape', 'im_id']
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dataset:
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!COCODataSet
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dataset_dir: dataset/coco
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anno_path: annotations/instances_val2017.json
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image_dir: val2017
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sample_transforms:
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- !DecodeImage
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to_rgb: true
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- !NormalizeBox {}
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- !ResizeImage
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interp: 1
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target_size: 320
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use_cv2: false
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- !NormalizeImage
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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is_scale: true
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is_channel_first: false
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- !Permute
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to_bgr: false
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channel_first: True
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batch_size: 8
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worker_num: 8
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bufsize: 32
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use_process: false
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TestReader:
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inputs_def:
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image_shape: [3,320,320]
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fields: ['image', 'im_id', 'im_shape']
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dataset:
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!ImageFolder
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anno_path: annotations/instances_val2017.json
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sample_transforms:
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- !DecodeImage
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to_rgb: true
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- !ResizeImage
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interp: 1
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max_size: 0
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target_size: 320
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use_cv2: true
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- !NormalizeImage
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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is_scale: true
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is_channel_first: false
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- !Permute
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to_bgr: false
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channel_first: True
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batch_size: 1
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