* The robust CNN-RNN Attention model utilizes ResNet-50 features, but consider enabling configurable fine-tuning of the final encoder layers. * Integrating `BertTokenizer` is effective; ensure the tokenizer's specific `pad_token_id` is consistently applied in the data collator and loss criteria. * The training script expertly incorporates Automatic Mixed Precision (AMP) via `GradScaler`, demonstrating adherence to modern PyTorch optimization practices. * Refactor hardcoded model dimensions and training hyperparameters currently scattered across `train.py` and `inference.py` into a single configuration file. * Strong separation of concerns is evident, partitioning the architecture, data loading, training loops, and inference into clean, reusable modules.
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