# global configs Global: checkpoints: null pretrained_model: null output_dir: ./output device: gpu save_interval: 1 eval_during_train: True eval_interval: 1 epochs: 60 print_batch_step: 10 use_visualdl: False # used for static mode and model export image_shape: [3, 224, 224] save_inference_dir: ./inference # model architecture Arch: name: MobileNetV3_small_x0_35 pretrained: True class_num: 2 # loss function config for traing/eval process Loss: Train: - CELoss: weight: 1.0 epsilon: 0.1 Eval: - CELoss: weight: 1.0 Optimizer: name: Momentum momentum: 0.9 lr: name: Cosine learning_rate: 0.4 warmup_epoch: 5 regularizer: name: 'L2' coeff: 0.00001 # data loader for train and eval DataLoader: Train: dataset: name: ImageNetDataset image_root: ./dataset/safety_helmet/ cls_label_path: ./dataset/safety_helmet/train_list.txt transform_ops: - DecodeImage: to_rgb: True channel_first: False - RandCropImage: size: 224 - RandFlipImage: flip_code: 1 - NormalizeImage: scale: 1.0/255.0 mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: '' sampler: name: DistributedBatchSampler batch_size: 64 drop_last: False shuffle: True loader: num_workers: 4 use_shared_memory: True Eval: dataset: name: ImageNetDataset image_root: ./dataset/safety_helmet/ cls_label_path: ./dataset/safety_helmet/val_list.txt transform_ops: - DecodeImage: to_rgb: True channel_first: False - ResizeImage: resize_short: 256 - CropImage: size: 224 - NormalizeImage: scale: 1.0/255.0 mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: '' sampler: name: DistributedBatchSampler batch_size: 64 drop_last: False shuffle: False loader: num_workers: 4 use_shared_memory: True Infer: infer_imgs: deploy/images/PULC/safety_helmet/safety_helmet_test_1.png batch_size: 1 transforms: - DecodeImage: to_rgb: True channel_first: False - ResizeImage: resize_short: 256 - CropImage: size: 224 - NormalizeImage: scale: 1.0/255.0 mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: '' - ToCHWImage: PostProcess: name: ThreshOutput threshold: 0.5 label_0: wearing_helmet label_1: unwearing_helmet Metric: Train: - TopkAcc: topk: [1] Eval: - TprAtFpr: max_fpr: 0.0001 - TopkAcc: topk: [1]