# Runtime use_gpu: true use_xpu: false use_mlu: false use_npu: false save_dir: output print_flops: false print_params: false use_ema: true log_iter: 100 snapshot_epoch: 4 # Dataset metric: COCO num_classes: 1 TrainDataset: name: COCODataSet image_dir: "" anno_path: annotations/train.json dataset_dir: dataset/crowdhuman data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd'] EvalDataset: name: COCODataSet image_dir: "" anno_path: annotations/val.json dataset_dir: dataset/crowdhuman allow_empty: true TestDataset: name: ImageFolder anno_path: annotations/val.json dataset_dir: dataset/crowdhuman #reader worker_num: 4 eval_height: &eval_height 640 eval_width: &eval_width 640 eval_size: &eval_size [*eval_height, *eval_width] TrainReader: sample_transforms: - Decode: {} - RandomDistort: {} - RandomExpand: {fill_value: [123.675, 116.28, 103.53]} - RandomCrop: {} - RandomFlip: {} batch_transforms: - BatchRandomResize: {target_size: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608, 640, 672, 704, 736, 768], random_size: True, random_interp: True, keep_ratio: False} - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True} - Permute: {} - PadGT: {} batch_size: 8 shuffle: true drop_last: true use_shared_memory: true collate_batch: true EvalReader: sample_transforms: - Decode: {} - Resize: {target_size: *eval_size, keep_ratio: False, interp: 2} - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True} - Permute: {} batch_size: 2 TestReader: inputs_def: image_shape: [3, *eval_height, *eval_width] sample_transforms: - Decode: {} - Resize: {target_size: *eval_size, keep_ratio: False, interp: 2} - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True} - Permute: {} batch_size: 1 # Model pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams depth_mult: 1.0 width_mult: 1.0 architecture: YOLOv3 norm_type: sync_bn use_ema: true ema_decay: 0.9998 ema_black_list: ['proj_conv.weight'] custom_black_list: ['reduce_mean'] YOLOv3: backbone: CSPResNet neck: CustomCSPPAN yolo_head: PPYOLOEHead post_process: ~ CSPResNet: layers: [3, 6, 6, 3] channels: [64, 128, 256, 512, 1024] return_idx: [1, 2, 3] use_large_stem: True CustomCSPPAN: out_channels: [768, 384, 192] stage_num: 1 block_num: 3 act: 'swish' spp: true PPYOLOEHead: fpn_strides: [32, 16, 8] grid_cell_scale: 5.0 grid_cell_offset: 0.5 static_assigner_epoch: -1 use_varifocal_loss: True loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5} static_assigner: name: ATSSAssigner topk: 9 assigner: name: TaskAlignedAssigner topk: 13 alpha: 1.0 beta: 6.0 nms: name: MultiClassNMS nms_top_k: 1000 keep_top_k: 100 score_threshold: 0.01 nms_threshold: 0.6 # Optimizer epoch: 36 LearningRate: base_lr: 0.001 schedulers: - name: CosineDecay max_epochs: 43 - name: LinearWarmup start_factor: 0. epochs: 1 OptimizerBuilder: optimizer: momentum: 0.9 type: Momentum regularizer: factor: 0.0005 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