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- #Config File example
- save_dir: workspace/nanodet_m
- model:
- weight_averager:
- name: ExpMovingAverager
- decay: 0.9998
- arch:
- name: NanoDetPlus
- detach_epoch: 10
- backbone:
- name: ShuffleNetV2
- model_size: 1.0x
- out_stages: [2,3,4]
- activation: LeakyReLU
- fpn:
- name: GhostPAN
- in_channels: [116, 232, 464]
- out_channels: 96
- kernel_size: 5
- num_extra_level: 1
- use_depthwise: True
- activation: LeakyReLU
- head:
- name: NanoDetPlusHead
- num_classes: 80
- input_channel: 96
- feat_channels: 96
- stacked_convs: 2
- kernel_size: 5
- strides: [8, 16, 32, 64]
- activation: LeakyReLU
- reg_max: 7
- norm_cfg:
- type: BN
- loss:
- loss_qfl:
- name: QualityFocalLoss
- use_sigmoid: True
- beta: 2.0
- loss_weight: 1.0
- loss_dfl:
- name: DistributionFocalLoss
- loss_weight: 0.25
- loss_bbox:
- name: GIoULoss
- loss_weight: 2.0
- # Auxiliary head, only use in training time.
- aux_head:
- name: SimpleConvHead
- num_classes: 80
- input_channel: 192
- feat_channels: 192
- stacked_convs: 4
- strides: [8, 16, 32, 64]
- activation: LeakyReLU
- reg_max: 7
-
- class_names: &class_names ['NAME1', 'NAME2', 'NAME3', 'NAME4', '...'] #Please fill in the category names (not include background category)
- data:
- train:
- name: XMLDataset
- class_names: *class_names
- img_path: TRAIN_IMAGE_FOLDER #Please fill in train image path
- ann_path: TRAIN_XML_FOLDER #Please fill in train xml path
- input_size: [320,320] #[w,h]
- keep_ratio: True
- pipeline:
- perspective: 0.0
- scale: [0.6, 1.4]
- stretch: [[1, 1], [1, 1]]
- rotation: 0
- shear: 0
- translate: 0.2
- flip: 0.5
- brightness: 0.2
- contrast: [0.8, 1.2]
- saturation: [0.8, 1.2]
- normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]]
- val:
- name: XMLDataset
- class_names: *class_names
- img_path: VAL_IMAGE_FOLDER #Please fill in val image path
- ann_path: VAL_XML_FOLDER #Please fill in val xml path
- input_size: [320,320] #[w,h]
- keep_ratio: True
- pipeline:
- normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]]
- device:
- gpu_ids: [0] # Set like [0, 1, 2, 3] if you have multi-GPUs
- workers_per_gpu: 8
- batchsize_per_gpu: 96
- schedule:
- # resume:
- # load_model: YOUR_MODEL_PATH
- optimizer:
- name: AdamW
- lr: 0.001
- weight_decay: 0.05
- warmup:
- name: linear
- steps: 500
- ratio: 0.0001
- total_epochs: 300
- lr_schedule:
- name: CosineAnnealingLR
- T_max: 300
- eta_min: 0.00005
- val_intervals: 10
- grad_clip: 35
- evaluator:
- name: CocoDetectionEvaluator
- save_key: mAP
-
- log:
- interval: 10
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