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@@ -34,3 +34,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 simclr_256_e800/20221227_183052.log.json filter=lfs diff=lfs merge=lfs -text
 simclr_256_e800/train_20221227_183052.log filter=lfs diff=lfs merge=lfs -text
+moco_256_e800_t15a05A02/20230110_110029.log.json filter=lfs diff=lfs merge=lfs -text
+moco_256_e800_t15a05A02/train_20230110_110029.log filter=lfs diff=lfs merge=lfs -text

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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