| # EfficientNetV2-M sınıflandırıcı eğitim parametreleri | |
| # Referans: arXiv 2505.04678 — EfficientNetV2M, 99.99% top-1 (235 sınıf, 14K veri) | |
| # Tan & Le (2021) — EfficientNetV2: Smaller Models and Faster Training | |
| model_name: tf_efficientnetv2_m | |
| epochs: 120 | |
| batch: 32 # GPU başına (V2M daha büyük → batch küçültüldü) | |
| lr: 0.0003 # Daha düşük LR — büyük model | |
| img_size: 224 # EfficientNetV2-M native çözünürlük | |
| patience: 30 | |
| device: 0 | |
| workers: 10 | |
| name: hitit_cls_effnetv2m | |
| # Loss | |
| loss: focal | |
| label_smoothing: 0.1 | |
| focal_gamma: 2.0 | |
| focal_alpha: 1.0 | |
| # Augmentation | |
| mixup: true | |
| mixup_alpha: 0.2 | |
| tailmix: true | |
| tailmix_threshold: 50 | |
| cutmix: true | |
| # Scheduler | |
| scheduler: cosine_warm_restarts | |
| T_0: 20 | |
| T_mult: 2 | |
| eta_min: 0.000001 | |
| # Sınıf dengesizliği | |
| use_weighted_sampler: true | |
| class_weight_method: effective_num | |
| # EfficientNet-specific | |
| efficientnet: | |
| drop_rate: 0.3 | |
| drop_path_rate: 0.2 | |
| layerwise_lr_decay: 0.85 # Katman bazlı LR decay — alt katmanlar düşük LR alır | |