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+# Training configuration for RTX 4090 (24GB VRAM) with IMAGE-LEVEL splits
+#
+# Combines RTX 4090 hardware optimizations with image-level splitting and
+# gentler contrastive learning for better generalization.
+#
+# Usage:
+#   python train_clip_logo.py --config configs/cloud_rtx4090_image_split.yaml
+#
+# Estimated training time: 5-7 hours (more epochs than logo-level)
+# Estimated cost on RunPod: ~$4
+
+# Base model
+base_model: "openai/clip-vit-large-patch14"
+
+# Dataset paths
+dataset_dir: "LogoDet-3K"
+reference_dir: "reference_logos"
+db_path: "test_data_mapping.db"
+
+# Data split configuration - IMAGE LEVEL
+# Each logo brand will have images in all splits, allowing the model
+# to see some examples of each brand during training.
+split_level: "image"
+train_split: 0.7
+val_split: 0.15
+test_split: 0.15
+
+# Larger batches for faster training on 24GB VRAM
+batch_size: 32
+logos_per_batch: 32
+samples_per_logo: 4
+gradient_accumulation_steps: 4  # Effective batch = 128
+num_workers: 8
+
+# Model architecture
+lora_r: 16
+lora_alpha: 32
+lora_dropout: 0.1
+freeze_layers: 12
+use_gradient_checkpointing: true
+
+# Training - GENTLER settings for better generalization
+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 - 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 - more patience with gentler learning
+patience: 7
+min_delta: 0.001
+
+# Output - separate directory for image-split model
+checkpoint_dir: "checkpoints_image_split"
+output_dir: "models/logo_detection/clip_finetuned_image_split"
+save_every_n_epochs: 2          # Save frequently for cloud
+
+# Logging
+log_every_n_steps: 10
+eval_every_n_epochs: 1
+
+seed: 42
+use_hard_negatives: false
+use_augmentation: true
+augmentation_strength: "medium"