# Training configuration with IMAGE-LEVEL splits
#
# Unlike logo-level splits where test logos are completely unseen brands,
# image-level splits allow the model to see some images from each brand
# during training. This is less rigorous but more representative of
# real-world use where you have reference images for logos you want to detect.
#
# Also uses gentler contrastive learning settings to prevent over-separation.
#
# Usage:
#   uv run python train_clip_logo.py --config configs/image_level_splits.yaml

# Base model
base_model: "openai/clip-vit-large-patch14"

# Dataset paths (relative to project root)
dataset_dir: "LogoDet-3K"
reference_dir: "reference_logos"
db_path: "test_data_mapping.db"

# Data split configuration
# split_level: "image" means images are split, not logo brands
# This allows test set to contain images from brands seen during training
split_level: "image"
train_split: 0.7
val_split: 0.15
test_split: 0.15

# Batch construction
batch_size: 16
logos_per_batch: 32
samples_per_logo: 4
gradient_accumulation_steps: 8
num_workers: 4

# Model architecture - same as before
lora_r: 16
lora_alpha: 32
lora_dropout: 0.1
freeze_layers: 12
use_gradient_checkpointing: true

# Training hyperparameters - GENTLER settings
learning_rate: 5.0e-6           # Reduced from 1e-5
weight_decay: 0.01
warmup_steps: 500
max_epochs: 30                  # More epochs with slower learning
mixed_precision: true

# Loss function - HIGHER temperature for softer contrastive learning
temperature: 0.15               # Increased from 0.07
loss_type: "infonce"
triplet_margin: 0.2             # Reduced from 0.3

# Early stopping
patience: 7                     # More patience with gentler learning
min_delta: 0.001

# Checkpoints and output
checkpoint_dir: "checkpoints_image_split"
output_dir: "models/logo_detection/clip_finetuned_image_split"
save_every_n_epochs: 5

# Logging
log_every_n_steps: 10
eval_every_n_epochs: 1

# Reproducibility
seed: 42

# Hard negative mining
use_hard_negatives: false
hard_negative_start_epoch: 10
hard_negatives_per_logo: 10

# Data augmentation
use_augmentation: true
augmentation_strength: "medium"