First project review

Prepare evidence, not invented results.

This initial implementation is complete when you can demonstrate the data path and show output generated from your own local dataset. Do not quote accuracy values until you have run the code.

Review checklist

0 of 6 preparation items marked.

Dataset is in dataset/<class_name>/image files layout.inspect_dataset.py ran and printed class names plus image counts.requirements.txt dependencies installed in an activated virtual environment.train.py printed model.summary() with a frozen EfficientNetV2 base.The two-epoch run produced real training/validation loss and accuracy curves.sample_predictions.png shows one or two actual held-out predictions.

Final run command

python train.py

Expected artifacts

training_curves.png, sample_predictions.png, and efficientnetv2_initial.keras are created only after the training run finishes.

Deliberately not included yet

CBAM attention, Grad-CAM++, fine-tuning, hyperparameter search, clinical validation, and deployment belong to later milestones.