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train.yaml
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train.yaml
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seed: 42
precision: 32
accelerator: cpu
devices: 2
find_unused_parameters: True
dataset_root: path_to_replace # we will replace it in runtime with the default dataset folder
dataframe_name: df.csv
logs_root: logs
num_workers: 0
cache_size: 10
transforms_train:
name: augs_torch
args:
im_size: 224
transforms_val:
name: norm_resize_torch
args:
im_size: 224
criterion:
name: arcface
args:
smoothing_epsilon: 0
m: 0.4
s: 64
in_features: 512
num_classes: 4
defaults:
- optimizer: sgd
- sampler: balance
extractor:
name: vit_clip
args:
arch: vitb16_224
weights: null
normalise_features: False
scheduling:
scheduler_interval: epoch
scheduler_frequency: 1
scheduler:
name: multi_step
args:
milestones:
- 1
- 2
- 3
bs_val: 2
metric_args:
metrics_to_exclude_from_visualization: [cmc, precision]
cmc_top_k: [1, 5]
map_top_k: [5]
precision_top_k: [1, 5]
return_only_overall_category: False
visualize_only_overall_category: True
log_images: True
metric_for_checkpointing: OVERALL/precision/5
max_epochs: 2
valid_period: 1
logger:
name: tensorboard
args:
save_dir: "."
lightning_trainer_extra_args:
max_time: "00:12:00:00" # 12h
tags:
- mock
hydra:
run:
dir: ${logs_root}
searchpath:
- pkg://oml.configs
job:
chdir: True