训练的mobilenetv3为什么最后在val的ap只有54%?我只将mobileentv3里的Hsigmoid改为了sigmoid

[zeckireck]

base = [ '../../../../base/default_runtime.py', '../../../../base/datasets/coco.py' ] evaluation = dict(interval=10, metric='mAP', save_best='AP')

optimizer = dict( type='Adam', lr=5e-4, ) optimizer_config = dict(grad_clip=None)

learning policy

lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=0.001, step=[170, 200]) total_epochs = 210 channel_cfg = dict( num_output_channels=17, dataset_joints=17, dataset_channel=[ [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], ], inference_channel=[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 ])

model settings

model = dict( type='TopDown', pretrained=None, backbone=dict(type='MobileNetV3', arch='small', out_indices=(10, )), keypoint_head=dict( type='TopdownHeatmapSimpleHead', in_channels=96, out_channels=channel_cfg['num_output_channels'], loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), train_cfg=dict(), test_cfg=dict( flip_test=True, post_process='default', shift_heatmap=True, modulate_kernel=11))

data_cfg = dict( image_size=[192, 256], heatmap_size=[48, 64], num_output_channels=channel_cfg['num_output_channels'], num_joints=channel_cfg['dataset_joints'], dataset_channel=channel_cfg['dataset_channel'], inference_channel=channel_cfg['inference_channel'], soft_nms=False, nms_thr=1.0, oks_thr=0.9, vis_thr=0.2, use_gt_bbox=False, det_bbox_thr=0.0, bbox_file='/data/datasets/annotations/' 'COCO_val2017_detections_AP_H_56_person.json', )

train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='TopDownGetBboxCenterScale', padding=1.25), dict(type='TopDownRandomShiftBboxCenter', shift_factor=0.16, prob=0.3), dict(type='TopDownRandomFlip', flip_prob=0.5), dict( type='TopDownHalfBodyTransform', num_joints_half_body=8, prob_half_body=0.3), dict( type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), dict(type='TopDownAffine'), dict(type='ToTensor'), dict( type='NormalizeTensor', mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), dict(type='TopDownGenerateTarget', sigma=2), dict( type='Collect', keys=['img', 'target', 'target_weight'], meta_keys=[ 'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', 'rotation', 'bbox_score', 'flip_pairs' ]), ]

val_pipeline = [ dict(type='LoadImageFromFile'), dict(type='TopDownGetBboxCenterScale', padding=1.25), dict(type='TopDownAffine'), dict(type='ToTensor'), dict( type='NormalizeTensor', mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), dict( type='Collect', keys=['img'], meta_keys=[ 'image_file', 'center', 'scale', 'rotation', 'bbox_score', 'flip_pairs' ]), ]

test_pipeline = val_pipeline

data_root = '/data/datasets/COCO' data = dict( samples_per_gpu=64, workers_per_gpu=2, val_dataloader=dict(samples_per_gpu=32), test_dataloader=dict(samples_per_gpu=32), train=dict( type='TopDownCocoDataset', ann_file=f'{data_root}/annotations/person_keypoints_train2017.json', img_prefix=f'{data_root}/train2017/', data_cfg=data_cfg, pipeline=train_pipeline, dataset_info={{base.dataset_info}}), val=dict( type='TopDownCocoDataset', ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', img_prefix=f'{data_root}/val2017/', data_cfg=data_cfg, pipeline=val_pipeline, dataset_info={{base.dataset_info}}), test=dict( type='TopDownCocoDataset', ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', img_prefix=f'{data_root}/val2017/', data_cfg=data_cfg, pipeline=test_pipeline, dataset_info={{base.dataset_info}}), )

[mm-assistant[bot]]

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[Tau-J]

从训练log来看结果是正常的，可能因为你没有使用基于imagenet的backbone预训练权重