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wandb_version: 1
_fields:
desc: null
value:
seed: 0
total_epochs: 90
num_classes: 1000
loss: softmax_xent
input: "accum_freq: 1\nbatch_size: 1024\ncache_raw: true\ndata:\n name: imagenet2012\n\
\ split: train\npp: decode_jpeg_and_inception_crop(224, method=\"bilinear\"\
, antialias=True, precise=True)|flip_lr|randaug(2,10)|value_range(-1,\n 1)|onehot(1000,\
\ key=\"label\", key_result=\"labels\")|keep(\"image\", \"labels\")\nshuffle_buffer_size:\
\ 1281167\n"
pp_modules:
- ops_general
- ops_image
- ops_text
- archive.randaug
log_training_steps: 50
ckpt_steps: 1000
model_name: vit
model: 'pool_type: gap
posemb: sincos2d
rep_size: false
variant: S/16
'
grad_clip_norm: 1.0
optax_name: scale_by_adam
optax: 'mu_dtype: float32
'
lr: 0.001
wd: 0.0001
schedule: 'decay_type: cosine
warmup_steps: 10000
'
mixup: 'fold_in: null
p: 0.2
'
evals: "val:\n data:\n name: imagenet2012\n split: validation\n log_steps:\
\ 2500\n loss_name: softmax_xent\n pp_fn: decode(precise=True)|resize_small(256,\
\ method=\"bilinear\", antialias=True)|central_crop(224)|value_range(-1,\n \
\ 1)|onehot(1000, key=\"label\", key_result=\"labels\")|keep(\"image\", \"\
labels\")\n type: classification\n"
_locked:
desc: null
value: true
_type_safe:
desc: null
value: true
_convert_dict:
desc: null
value: true
_wandb:
desc: null
value:
python_version: 3.10.12
cli_version: 0.17.3
framework: keras
is_jupyter_run: false
is_kaggle_kernel: false
start_time: 1719611846
t:
1:
- 2
- 3
- 12
- 45
- 55
2:
- 2
- 3
- 12
- 45
- 55
3:
- 13
- 14
- 16
- 23
- 61
4: 3.10.12
5: 0.17.3
8:
- 5
13: linux-x86_64