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sweep_neurips_db.yml
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sweep_neurips_db.yml
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program: train.py
method: grid
metric:
goal: minimize
name: eval_loss
parameters:
model:
values: [resnet18, resnet18_ciga_ssl, resnet50, vit_small_r26_s32_224]
pretrained:
# The train.py script will skip the not pretrained resnet18_ciga_ssl. It doesn't
# make sense to test non-pretrained resnet18_ciga_ssl because that is the same
# as resnet18 from scratch.
values: [true, false]
data-dir:
values:
- data/task1_tumor_notumor/images/
- data/task2_tils/images-tcga-tils/pancancer/
- data/task3_msi_crc_ffpe/images/
- data/task4_msi_crc_frozen/images/
- data/task5_msi_stad_ffpe/images/
- data/task6_precancer_vs_benign_polyps/images-split/
seed:
value: 42
lr:
value: 0.0001
freeze-encoder:
value: false
epochs:
value: 500
sched:
value: cosine
opt:
value: adamw
opt-eps:
value: .001
warmup-lr:
value: 1e-6
weight-decay:
value: 0.01
drop:
value: 0.3
drop-path:
value: 0.2
model-ema:
value: true
model-ema-decay:
value: 0.9999
aa:
value: rand-m9-mstd0.5
remode:
value: pixel
reprob:
value: 0.2
amp:
value: true
train-split:
value: train
val-split:
value: val
workers:
value: 4
num-classes:
value: 2
batch-size:
value: 84
decay-epochs:
value: 2.4
decay-rate:
value: .97
log-wandb:
value: true
experiment:
value: neuripsdb
output:
value: sweep-output/
command:
- ${env}
- ${interpreter}
- ${program}
- ${args_no_boolean_flags}