# Runtime find_unused_parameters: True use_ema: true use_gpu: true use_xpu: false use_mlu: false use_npu: false log_iter: 20 save_dir: output snapshot_epoch: 10 print_flops: false print_params: false # Dataset metric: COCO num_classes: 80 TrainDataset: name: COCODataSet image_dir: train2017 anno_path: annotations/instances_train2017.json dataset_dir: dataset/coco data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd'] EvalDataset: name: COCODataSet image_dir: val2017 anno_path: annotations/instances_val2017.json dataset_dir: dataset/coco allow_empty: true TestDataset: name: ImageFolder anno_path: annotations/instances_val2017.json # also support txt (like VOC's label_list.txt) dataset_dir: dataset/coco # if set, anno_path will be 'dataset_dir/anno_path' # Reader worker_num: 6 eval_height: &eval_height 416 eval_width: &eval_width 416 eval_size: &eval_size [*eval_height, *eval_width] TrainReader: sample_transforms: - Decode: {} - RandomCrop: {} - RandomFlip: {prob: 0.5} - RandomDistort: {} batch_transforms: - BatchRandomResize: {target_size: [352, 384, 416, 448, 480], random_size: True, random_interp: True, keep_ratio: False} - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]} - Permute: {} - PadGT: {} batch_size: 48 shuffle: true drop_last: true EvalReader: sample_transforms: - Decode: {} - Resize: {interp: 2, target_size: *eval_size, keep_ratio: False} - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]} - Permute: {} batch_transforms: - PadBatch: {pad_to_stride: 32} batch_size: 8 shuffle: false TestReader: inputs_def: image_shape: [3, *eval_height, *eval_width] sample_transforms: - Decode: {} - Resize: {interp: 2, target_size: *eval_size, keep_ratio: False} - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]} - Permute: {} batch_size: 1 # Model architecture: PicoDet pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/LCNet_x1_5_pretrained.pdparams PicoDet: backbone: LCNet neck: LCPAN head: PicoHeadV2 LCNet: scale: 1.5 feature_maps: [3, 4, 5] LCPAN: out_channels: 128 use_depthwise: True num_features: 4 PicoHeadV2: conv_feat: name: PicoFeat feat_in: 128 feat_out: 128 num_convs: 4 num_fpn_stride: 4 norm_type: bn share_cls_reg: True use_se: True fpn_stride: [8, 16, 32, 64] feat_in_chan: 128 prior_prob: 0.01 reg_max: 7 cell_offset: 0.5 grid_cell_scale: 5.0 static_assigner_epoch: 100 use_align_head: True static_assigner: name: ATSSAssigner topk: 9 force_gt_matching: False assigner: name: TaskAlignedAssigner topk: 13 alpha: 1.0 beta: 6.0 loss_class: name: VarifocalLoss use_sigmoid: False iou_weighted: True loss_weight: 1.0 loss_dfl: name: DistributionFocalLoss loss_weight: 0.5 loss_bbox: name: GIoULoss loss_weight: 2.5 nms: name: MultiClassNMS nms_top_k: 1000 keep_top_k: 100 score_threshold: 0.025 nms_threshold: 0.6 # Optimizer epoch: 250 LearningRate: base_lr: 0.24 schedulers: - name: CosineDecay max_epochs: 300 - name: LinearWarmup start_factor: 0.1 steps: 300 OptimizerBuilder: optimizer: momentum: 0.9 type: Momentum regularizer: factor: 0.00004 type: L2 # Exporting the model export: post_process: True # Whether post-processing is included in the network when export model. nms: True # Whether NMS is included in the network when export model. benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported. fuse_conv_bn: False