Dataset discussion: German Traffic Signs

Part 1 (2017)
6

From lesson 3, I’d say don’t just look at validation accuracy. Compare it to the training accuracy to see whether you’re overfitting or underfitting, and then act accordingly. See lesson 3 on how to deal with either situation.

Update: From 62.15% to 93.51% accuracy

Differences:

  • Instead of fine-tuning VGG, I used the VGG-style CNN with batchnorm from the MNIST notebook.
    • I think this worked better because traffic signs are must simpler than cats, dogs, flowers, and so on. This allowed for a simpler network like the one Jeremy and Rachel used for MNIST. The simplicity of the network allowed me to retrain all the filters instead of fine-tuning them. One epoch from random weights took 51 seconds.
  • Only did one epoch! (see TODO) (learning_rate == 0.001)

TODO:

  • Apply more lesson 3 skills/ideas/tips/tricks:
    • Do more than one epoch!
      • Use the learning rate pattern that Jeremy used (0.001, 0.1, 0.01, 0.001, …)
    • Data augmentation
    • Dropout
    • Ensemble
  • Input normalization (I didn’t subtract the mean or divide by the standard deviation)

Update: From 93.51% accuracy to 97.58% accuracy

Differences:

  • 30 epochs instead of 1 (same learning rate, 0.001)

TODO:

  • Same TODO
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