A simple synthetic dataset and baseline model for visual counting.
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counting_mnist
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Distribution.ipynb
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README.md
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README.md

Counting MNIST

A simple synthetic dataset and baseline model for visual counting. The task is to count the number of even digits given a 100x100 image, each with up to 5 randomly chosen MNIST digits. We use rejection sampling to ensure digits are separated by at least 28 pixels. Reproduced with details from Learning to count with deep object features.

NOTE: This is not a dataset to beat, but a simple place to start for validating ideas in counting models.

Sample

Distribution

Instructions

  1. Generate TFRecords:
python -m counting_mnist.create_dataset
  1. Train baseline:
python -m counting_mnist.main

Results

Model Accuracy
Always Predict Zero Even Digits 33%
Uniform Count Predictions 12%
Baseline Model 85%