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config.yml
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# Configuration file for feature extraction and metric computation
feature_extraction: false
eval_only: false
sing_image_eval: false
realism_correlation: true
saliency_representation: false
privacy_benchmark: false
adversarial_privacy_assesment: false
degraded_datasets: false
# Paths to the real and synthetic datasets
real_dataset_path: '/mnt/DV-MICROK/Syn.Dat/Marc/GitLab/datasets/CSAW/images/hg_dataset/train'
synthetic_dataset_path: '/mnt/DV-MICROK/Syn.Dat/Marc/GitLab/syntheva/logs/014-DiT-XL-2/full_synthetic_csaw'
dataset_info: 'data/chestxpert_small.csv'
output_preprocessing: './data/preprocessing/'
unique_individual_id: null
secondary_ids: null
unique_image_id: null
image_extension: '.png'
seed: 42
model_to_seek:
- 'diff'
- 'gan'
# List of networks to use for feature extraction
networks:
- 'rad_inception'
- 'inception'
- 'resnet50'
- 'rad_resnet50'
#- 'inceptionresnet'
#- 'rad_inceptionresnet'
- 'densenet121'
- 'rad_densenet'
- 'clip'
- 'rad_clip'
- 'rad_dino'
- 'dino'
#- 'ijepa'
metrics:
- fid
- precision
- recall
- density
- coverage
- kid
- is
- mmd
- fid_inf
- realism
- vendi
- authpct
- sw
# Batch size for feature extraction
batch_size: 32
inference_bs: 768
#Number of sets to divide each dataset into
num_sets: 10
# Directory to save the metrics results
metrics_output_dir: 'metrics'
# Directory to save features and metrics
features_output_dir: 'data/features'
# Path to jsonl where turign test responses are contained
jsonl_path:
- '/home/ksamamov/GitLab/Notebooks/evaluations_b22897e1-11c3-4ed3-b80f-d19670607bd5_Nadine2_Benz2.jsonl'
- '/home/ksamamov/GitLab/Notebooks/feat_ext_bench/data/turing_tests/evaluations_e15ce52f-ab76-45f2-9b94-8a7e140c3bbb_DANIELA_RAMIREZ.jsonl'
timestamp: '20240930_150033' #'20240821_151647' # for specific features previously computed, use with feature_extraction=false
do_z_score: true
preprocess_to_npy: True
# New parameters for privacy benchmark
ijepa_model_dir: './ijepa/logs/in2kk_vith14.224-bs.2048-ep.66'
n_features: 128
target_resolution: [224, 224]
split_ratio: 0.8
num_workers: 4
pin_memory: true
base_lr: 0.01
min_lr: 0.00005
gamma: 0.99
n_epochs: 50
temperature: 0.5
save_model_interval: 10
multi_gpu: true
loss_type: 'triplet' # New parameter to choose between 'ntxent' and 'margin'
margin: 0.05
swap: false,
smooth_loss: true,
triplets_per_anchor: 'all'
unfreeze_epoch: 5
downscaling_after_freezing: 200
attentive_probing: False
augmentation_strength: 'weak' # or 'strong'
contrastive: true
ema_momentum: 0.99
ema_momentum_end: 1.0
projection_dim: 128
prediction_dim: 128
feature_dim: 128