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‎config/LiquidDetect.yml‎

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

‎config/LiquidDetect416.yml‎

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#Config File example
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save_dir: workspace/lqd
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model:
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weight_averager:
5+
name: ExpMovingAverager
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decay: 0.9998
7+
arch:
8+
name: NanoDetPlus
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detach_epoch: 10
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backbone:
11+
name: ShuffleNetV2
12+
model_size: 1.0x
13+
out_stages: [2,3,4]
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activation: LeakyReLU
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fpn:
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name: GhostPAN
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in_channels: [116, 232, 464]
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out_channels: 96
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kernel_size: 5
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num_extra_level: 1
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use_depthwise: True
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activation: LeakyReLU
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head:
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name: NanoDetPlusHead
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num_classes: 1
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input_channel: 96
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feat_channels: 96
28+
stacked_convs: 2
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kernel_size: 5
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strides: [8, 16, 32, 64]
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activation: LeakyReLU
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reg_max: 7
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norm_cfg:
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type: BN
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loss:
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loss_qfl:
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name: QualityFocalLoss
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use_sigmoid: True
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beta: 2.0
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loss_weight: 1.0
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loss_dfl:
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name: DistributionFocalLoss
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loss_weight: 0.25
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loss_bbox:
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name: GIoULoss
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loss_weight: 2.0
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# Auxiliary head, only use in training time.
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aux_head:
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name: SimpleConvHead
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num_classes: 1
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input_channel: 192
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feat_channels: 192
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stacked_convs: 4
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strides: [8, 16, 32, 64]
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activation: LeakyReLU
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reg_max: 7
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class_names: &class_names ['Liquid'] #Please fill in the category names (not include background category)
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data:
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train:
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name: XMLDataset
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class_names: *class_names
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img_path: lq/train/img
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ann_path: lq/train/an
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input_size: [416,416] #[w,h]
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keep_ratio: True
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pipeline:
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perspective: 0.0
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scale: [0.6, 1.4]
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stretch: [[1, 1], [1, 1]]
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rotation: 0
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shear: 0
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translate: 0.2
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flip: 0.5
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brightness: 0.2
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contrast: [0.8, 1.2]
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saturation: [0.8, 1.2]
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normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]]
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val:
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name: XMLDataset
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class_names: *class_names
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img_path: lq/valid/img
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ann_path: lq/valid/an
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input_size: [416,416] #[w,h]
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keep_ratio: True
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pipeline:
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normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]]
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device:
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gpu_ids: [0] # Set like [0, 1, 2, 3] if you have multi-GPUs
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workers_per_gpu: 8
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batchsize_per_gpu: 4
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schedule:
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# resume:
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# load_model: YOUR_MODEL_PATH
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optimizer:
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name: AdamW
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lr: 0.001
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weight_decay: 0.05
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warmup:
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name: linear
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steps: 500
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ratio: 0.0001
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total_epochs: 300
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lr_schedule:
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name: CosineAnnealingLR
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T_max: 300
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eta_min: 0.00005
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val_intervals: 10
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grad_clip: 35
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evaluator:
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name: CocoDetectionEvaluator
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save_key: mAP
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log:
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interval: 10

‎config/convnext/nanodet-plus_convnext-nano_640.yml‎

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save_dir: workspace/convnext/nanodet-plus_convnext-nano_640
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model:
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weight_averager:
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name: ExpMovingAverager
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decay: 0.9998
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arch:
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name: NanoDetPlus
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detach_epoch: 10
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backbone:
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name: TIMMWrapper
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model_name: convnext_nano
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features_only: True
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pretrained: True
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# output_stride: 32
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out_indices: [1, 2, 3]
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fpn:
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name: GhostPAN
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in_channels: [160, 320, 640]
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out_channels: 128
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kernel_size: 5
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num_extra_level: 1
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use_depthwise: True
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activation: SiLU
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head:
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name: NanoDetPlusHead
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num_classes: 80
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input_channel: 128
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feat_channels: 128
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stacked_convs: 2
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kernel_size: 5
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strides: [8, 16, 32, 64]
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activation: SiLU
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reg_max: 7
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norm_cfg:
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type: BN
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loss:
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loss_qfl:
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name: QualityFocalLoss
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use_sigmoid: True
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beta: 2.0
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loss_weight: 1.0
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loss_dfl:
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name: DistributionFocalLoss
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loss_weight: 0.25
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loss_bbox:
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name: GIoULoss
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loss_weight: 2.0
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# Auxiliary head, only use in training time.
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aux_head:
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name: SimpleConvHead
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num_classes: 80
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input_channel: 256
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feat_channels: 256
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stacked_convs: 4
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strides: [8, 16, 32, 64]
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activation: SiLU
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reg_max: 7
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data:
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train:
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name: CocoDataset
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img_path: coco/train2017
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ann_path: coco/annotations/instances_train2017.json
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input_size: [640,640] #[w,h]
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keep_ratio: False
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pipeline:
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perspective: 0.0
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scale: [0.1, 2.0]
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stretch: [[0.8, 1.2], [0.8, 1.2]]
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rotation: 0
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shear: 0
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translate: 0.2
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flip: 0.5
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brightness: 0.2
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contrast: [0.6, 1.4]
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saturation: [0.5, 1.2]
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normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]]
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val:
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name: CocoDataset
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img_path: coco/val2017
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ann_path: coco/annotations/instances_val2017.json
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input_size: [640,640] #[w,h]
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keep_ratio: False
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pipeline:
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normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]]
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device:
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gpu_ids: [0, 1, 2, 3]
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workers_per_gpu: 8
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batchsize_per_gpu: 24
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schedule:
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# resume:
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# load_model:
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optimizer:
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name: AdamW
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lr: 0.001
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weight_decay: 0.05
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no_norm_decay: True
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param_level_cfg:
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backbone:
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lr_mult: 0.1
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warmup:
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name: linear
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steps: 500
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ratio: 0.0001
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total_epochs: 50
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lr_schedule:
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name: CosineAnnealingLR
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T_max: 50
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eta_min: 0.0005
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val_intervals: 5
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grad_clip: 35
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evaluator:
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name: CocoDetectionEvaluator
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save_key: mAP
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log:
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interval: 50
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class_names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
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'train', 'truck', 'boat', 'traffic_light', 'fire_hydrant',
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'stop_sign', 'parking_meter', 'bench', 'bird', 'cat', 'dog',
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'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
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'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
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'skis', 'snowboard', 'sports_ball', 'kite', 'baseball_bat',
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'baseball_glove', 'skateboard', 'surfboard', 'tennis_racket',
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'bottle', 'wine_glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
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'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
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'hot_dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
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'potted_plant', 'bed', 'dining_table', 'toilet', 'tv', 'laptop',
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'mouse', 'remote', 'keyboard', 'cell_phone', 'microwave',
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'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock',
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'vase', 'scissors', 'teddy_bear', 'hair_drier', 'toothbrush']

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