|
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 |
|
|