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neptune.yaml
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#project: USERNAME/PROJECT
name: airbus_ships_challenge
tags: [solution-4]
metric:
channel: 'f2'
goal: maximize
#Comment out if not in Cloud Environment
#pip-requirements-file: requirements.txt
exclude:
- .git
- .idea
- .ipynb_checkpoints
- output
- imgs
- neptune.log
- offline_job.log
- notebooks
- big-images-ids.csv
- big-images-ids_v2.csv
parameters:
# Data Paths
train_images_dir: /public/challenges/kaggle-ship-detection/train
test_images_dir: /public/challenges/kaggle-ship-detection/test
metadata_filepath: /output/metadata.csv
annotation_file: /public/challenges/kaggle-ship-detection/train_ship_segmentations.csv
masks_overlayed_dir: /output/masks_overlayed
# Execution
num_workers: 4
num_threads: 100
image_source: disk
pin_memory: 1
loader_mode: resize
resize_target_size: 256
pad_method: symmetric
target_format: 'joblib'
dev_mode_size: 1000
# General parameters
image_h: 256
image_w: 256
image_channels: 3
training_sampler_size: 7500
training_sampler_empty_fraction: 0.0
evaluation_size: 10000
evaluation_empty_fraction: 0.52
in_train_evaluation_size: 500
fine_tuning: 1
# Network parameters
network_output_channels: 2
network_activation: 'sigmoid'
architecture: LargeKernelMatters
encoder: DenseNet
# Training schedule
epochs_nr: 1000
batch_size_train: 16
batch_size_inference: 16
lr: 0.0003
momentum: 0.9
gamma: 0.95
patience: 20
validation_metric_name: 'f2'
minimize_validation_metric: 0
reduce_factor: 0.5
reduce_patience: 5
min_lr: 0
use_one_cycle: 1
one_cycle_number_of_batches_per_full_cycle: 20000
one_cycle_max_lr: 0.05
# Regularization
use_batch_norm: 1
l2_reg_conv: 0.0001
# Postprocessing
threshold_masks: 0.44
tta_aggregation_method: mean
postpro__drop_size: 100
postpro__mid_min_size: 0
postpro__mid_max_size: 0
# -------------------- SNS (SHIP NO SHIP) ----------------------
# Network parameters
sns_architecture: Densenet
# Training schedule
sns_training_sampler_empty_fracion: 0.52
sns_epochs_nr: 100
sns_batch_size_train: 64
sns_batch_size_inference: 256
sns_lr: 0.0001
sns_momentum: 0.9
sns_gamma: 0.95
sns_patience: 5
sns_validation_metric_name: 'sum'
sns_minimize_validation_metric_name: 'sum'
sns_minimize_validation_metric: 1
sns_reduce_factor: 0.5
sns_reduce_patience: 10
sns_min_lr: 0
sns_use_one_cycle: 1
sns_one_cycle_number_of_batches_per_full_cycle: 10000
sns_one_cycle_max_lr: 0.0001
sns_image_h: 224
sns_image_w: 224
# Regularization
sns_use_batch_norm: 1
sns_l2_reg_conv: 0.0001
# Postprocessing
sns_threshold: 0.6