Delete moco_256_e800_t15a05A02

moco_256_e800_t15a05A02/20230110_110029.log.json

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moco_256_e800_t15a05A02/latest.pth

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moco_256_e800_t15a05A02/mocov2_macl_resnet50_256-coslr-800e.py

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-model = dict(
-    type='MoCo',
-    queue_len=65536,
-    feat_dim=128,
-    momentum=0.999,
-    backbone=dict(
-        type='ResNet',
-        depth=50,
-        in_channels=3,
-        out_indices=[4],
-        norm_cfg=dict(type='BN')),
-    neck=dict(
-        type='MoCoV2Neck',
-        in_channels=2048,
-        hid_channels=2048,
-        out_channels=128,
-        with_avg_pool=True),
-    head=dict(type='MaclaHead', temperature=0.15))
-data_source = 'ImageNet'
-dataset_type = 'MultiViewDataset'
-img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-train_pipeline = [
-    dict(type='RandomResizedCrop', size=224, scale=(0.2, 1.0)),
-    dict(
-        type='RandomAppliedTrans',
-        transforms=[
-            dict(
-                type='ColorJitter',
-                brightness=0.4,
-                contrast=0.4,
-                saturation=0.4,
-                hue=0.1)
-        ],
-        p=0.8),
-    dict(type='RandomGrayscale', p=0.2),
-    dict(type='GaussianBlur', sigma_min=0.1, sigma_max=2.0, p=0.5),
-    dict(type='RandomHorizontalFlip'),
-    dict(type='ToTensor'),
-    dict(
-        type='Normalize',
-        mean=[0.485, 0.456, 0.406],
-        std=[0.229, 0.224, 0.225])
-]
-prefetch = False
-data = dict(
-    samples_per_gpu=128,
-    workers_per_gpu=8,
-    drop_last=True,
-    train=dict(
-        type='MultiViewDataset',
-        data_source=dict(
-            type='ImageNet',
-            data_prefix='./data/train',
-            ann_file='./data/train.txt'),
-        num_views=[2],
-        pipelines=[[{
-            'type': 'RandomResizedCrop',
-            'size': 224,
-            'scale': (0.2, 1.0)
-        }, {
-            'type':
-            'RandomAppliedTrans',
-            'transforms': [{
-                'type': 'ColorJitter',
-                'brightness': 0.4,
-                'contrast': 0.4,
-                'saturation': 0.4,
-                'hue': 0.1
-            }],
-            'p':
-            0.8
-        }, {
-            'type': 'RandomGrayscale',
-            'p': 0.2
-        }, {
-            'type': 'GaussianBlur',
-            'sigma_min': 0.1,
-            'sigma_max': 2.0,
-            'p': 0.5
-        }, {
-            'type': 'RandomHorizontalFlip'
-        }, {
-            'type': 'ToTensor'
-        }, {
-            'type': 'Normalize',
-            'mean': [0.485, 0.456, 0.406],
-            'std': [0.229, 0.224, 0.225]
-        }]],
-        prefetch=False))
-optimizer = dict(type='SGD', lr=0.03, weight_decay=0.0001, momentum=0.9)
-optimizer_config = dict()
-lr_config = dict(policy='CosineAnnealing', min_lr=0.0)
-runner = dict(type='EpochBasedRunner', max_epochs=800)
-checkpoint_config = dict(interval=10, max_keep_ckpts=3)
-log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
-dist_params = dict(backend='nccl')
-cudnn_benchmark = True
-log_level = 'INFO'
-load_from = None
-resume_from = None
-workflow = [('train', 1)]
-persistent_workers = True
-opencv_num_threads = 0
-mp_start_method = 'fork'
-work_dir = 'trained/pretrain/moco_256_e200/'
-auto_resume = False
-gpu_ids = range(0, 2)

moco_256_e800_t15a05A02/train_20230110_110029.log

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