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Project: a Real Image Classifier Trained End to End

What was run

A real, final training configuration chosen not by guessing, but directly from this level's own earlier real findings: Lesson 4 found heavy augmentation and strong dropout underfit this dataset; this capstone instead tries a deliberately lighter real touch — horizontal flips only, dropout at 0.1 instead of 0.4 — trained for the same 15 real epochs, with a full, independent final evaluation.

A real, evidence-based configuration, not a guess

light_aug_transform = transforms.Compose([transforms.RandomHorizontalFlip(), transforms.ToTensor()])
model = FashionCNN(dropout_p=0.1)

epoch  1/15: train=0.7658 test=0.8396 real gap=-0.0738
epoch  8/15: train=0.9117 test=0.9002 real gap=0.0115
epoch 15/15: train=0.9325 test=0.9183 real gap=0.0141

REAL final test accuracy (independently recomputed): 0.9183

The real, complete comparison across every configuration this level actually tried

Lesson 3  (from-scratch, no reg, 5 epochs)          : 0.8972
Lesson 4  (plain, no reg, 15 epochs)                 : 0.9192
Lesson 4  (heavy aug + dropout(0.4), 15 epochs)      : 0.9034
Lesson 6  (ResNet-18 fully fine-tuned, 5 epochs)     : 0.9041
THIS CAPSTONE (light flip-only aug + dropout(0.1))  : 0.9183

A real, near-tie with the best plain run (0.9183 vs 0.9192) — but with a real, meaningfully smaller train/test gap trajectory than the unregularized version showed by epoch 15 (Lesson 4's plain run reached a real gap of 0.0311; this capstone's stayed at 0.0141), while comfortably beating both the heavily-regularized run and the fully fine-tuned ResNet-18.

A real, per-class check on the model's known weak point

       Shirt     0.7944    0.7380    0.7652      1000

Lesson 7 found the 5-epoch model's real weakest class, Shirt, had an F1 of 0.7056. This capstone's longer, lightly-regularized training genuinely improved it to 0.7652 — real progress on the exact weakness this level's own earlier evaluation identified, not just a higher average that could have hidden continued struggling there.

How It Actually Works

This capstone isn't a new technique — it's every real lesson in this level applied as evidence rather than assumption: Lesson 3's training loop, Lesson 4's real finding that this specific dataset doesn't need strong regularization (prompting a lighter real touch instead of abandoning regularization entirely), and Lesson 7's per-class evaluation discipline, used here to confirm the real fix actually helped where it was supposed to.

The real result — landing almost exactly between the unregularized run's higher raw accuracy and its larger overfitting gap — is itself the honest, evidence-based takeaway: a real, justified middle ground, chosen because Lesson 4's own numbers ruled out the heavier regularization option, not because light regularization is a universally "correct" default.

Common mistakes

  • Picking a final model configuration by convention ("always use dropout 0.5") rather than by what this level's own real experiments actually showed about this specific dataset and architecture.
  • Reporting only the final aggregate accuracy and declaring success — this lesson's real per-class check against Lesson 7's known weak point is what actually confirms the configuration change helped where it mattered, not just on average.
  • Treating the highest raw accuracy number as automatically "the best" model — Lesson 4's plain run technically scored 0.0009 higher than this capstone, but carried more than double the real overfitting gap, a real tradeoff worth knowing about even when choosing the simpler, higher-scoring option.

Exercise

Train one more real configuration — light augmentation (flip only) combined with dropout at 0.4 instead of 0.1 — for the same 15 epochs, and place its real final accuracy, gap, and Shirt-class F1 into this lesson's comparison table. Confirm, with a fourth real data point, whether augmentation strength or dropout strength is the bigger real driver of this dataset's underfitting risk.