RT-ODLab/config/yolov3_config.py

# YOLOv3 Config

yolov3_cfg = {
    # input
    'trans_type': 'yolov5',
    'multi_scale': [0.5, 1.0],
    # model
    'backbone': 'darknet53',
    'pretrained': True,
    'stride': [8, 16, 32],  # P3, P4, P5
    'width': 1.0,
    'depth': 1.0,
    # neck
    'neck': 'sppf',
    'expand_ratio': 0.5,
    'pooling_size': 5,
    'neck_act': 'silu',
    'neck_norm': 'BN',
    'neck_depthwise': False,
     # fpn
    'fpn': 'yolo_fpn',
    'fpn_act': 'silu',
    'fpn_norm': 'BN',
    'fpn_depthwise': False,
    # head
    'head': 'decoupled_head',
    'head_act': 'silu',
    'head_norm': 'BN',
    'num_cls_head': 2,
    'num_reg_head': 2,
    'head_depthwise': False,
    'anchor_size': [[10, 13],   [16, 30],   [33, 23],     # P3
                    [30, 61],   [62, 45],   [59, 119],    # P4
                    [116, 90],  [156, 198], [373, 326]],  # P5
    # matcher
    'iou_thresh': 0.5,
    # loss weight
    'loss_obj_weight': 1.0,
    'loss_cls_weight': 1.0,
    'loss_box_weight': 5.0,
    # training configuration
    'no_aug_epoch': 10,
    # optimizer
    'optimizer': 'sgd',        # optional: sgd, adam, adamw
    'momentum': 0.937,         # SGD: 0.937;    AdamW: invalid
    'weight_decay': 5e-4,      # SGD: 5e-4;     AdamW: 5e-2
    'clip_grad': 10,           # SGD: 10.0;     AdamW: -1
    # model EMA
    'ema_decay': 0.9999,       # SGD: 0.9999;   AdamW: 0.9998
    'ema_tau': 2000,
    # lr schedule
    'scheduler': 'linear',
    'lr0': 0.01,               # SGD: 0.01;     AdamW: 0.004
    'lrf': 0.01,               # SGD: 0.01;     AdamW: 0.05
    'warmup_momentum': 0.8,
    'warmup_bias_lr': 0.1,
}