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- data:
- batch_size: 256
- pin_memory: true
- num_workers: 6
- # Thomas said it should be at least about 5-10x your batch size; beyond that,
- # the differences become academic.
- shuffle_buffer: 10000
- seed: ${train.seed}
- dataset:
- meta:
- gcc3m:
- type: img_txt_pair
- path: local_data/gcc3m_shards
- prefix: gcc-train-{000000..00436}.tar
- length: 2891445
- gcc12m:
- type: img_txt_pair
- path: local_data/gcc12m_shards
- prefix: gcc-conceptual-12m-{000000..001943}.tar
- length: 11156203
- yfcc14m:
- type: img_txt_pair
- path: local_data/yfcc14m_shards
- prefix: yfcc14m-{000000..001888}.tar
- length: 14615499
- redcap12m:
- type: img_txt_pair
- path: local_data/redcap12m_shards
- prefix: redcap12m-{000000..001211}.tar
- length: 11866987
- imagenet:
- type: img_cls_pair
- path: local_data/imagenet_shards
- prefix: imagenet-val-{000000..000049}.tar
- length: 50000
- train:
- - gcc3m
- - gcc12m
- - yfcc14m
- val:
- - imagenet
- img_aug:
- deit_aug: true
- img_size: 224
- img_scale: [0.08, 1.0]
- interpolation: bilinear
- color_jitter: 0.4
- auto_augment: 'rand-m9-mstd0.5-inc1'
- re_prob: 0.25
- re_mode: 'pixel'
- re_count: 1
- text_aug:
- max_seq_len: 77
- multi_label: 0
- word_type: 'noun'
- train:
- start_epoch: 0
- epochs: 30
- warmup_epochs: 2
- base_lr: 1.6e-3
- weight_decay: 0.05
- warmup_lr: 4e-6
- min_lr: 4e-5
- clip_grad: 5.0
- accumulation_steps: 0
- amp_opt_level: O1
- seed: 0
- lr_scheduler:
- name: cosine
- optimizer:
- name: adamw
- eps: 1e-8
- betas: [0.9, 0.999]
- evaluate:
- eval_only: false
- eval_freq: 1
- task:
- - cls
- - seg
- cls:
- save_best: true
- template: subset
- seg:
- save_best: true
- cfg: segmentation/configs/_base_/datasets/pascal_voc12.py
- template: simple
- opts: []
- checkpoint:
- auto_resume: true
- resume: ''
- freq: 1
- max_kept: -1
- save_freq: 1
- model_name: '' # display name in the logger
- output: ???
- tag: default
- print_freq: 10
- seed: 0
- wandb: false
- local_rank: ???
- vis: []
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