RT-ODLab/config/yolox_config.py

# YOLOx Config

yolox_cfg = {
    # input
    'trans_type': 'yolov5_strong',
    'multi_scale': [0.5, 1.0],
    # model
    'backbone': 'cspdarknet',
    'pretrained': True,
    'bk_act': 'silu',
    'bk_norm': 'BN',
    'bk_dpw': False,
    'stride': [8, 16, 32],  # P3, P4, P5
    'width': 1.0,
    'depth': 1.0,
     # fpn
    'fpn': 'yolo_pafpn',
    '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,
    # matcher
    'matcher': {'center_sampling_radius': 2.5,
                'topk_candicate': 10},
    # loss weight
    'loss_obj_weight': 1.0,
    'loss_cls_weight': 1.0,
    'loss_box_weight': 5.0,
    # training configuration
    'no_aug_epoch': 20,
    # 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,
}