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This project connects Kaggle’s Digit Recognizer data to an Android app that classifies a newly drawn digit. Train and validate a model against labeled examples, adapt it for TensorFlow Lite inference, then use Antigravity CLI from a documented desktop or server environment to help work on the code. Kaggle competition predictions and an interactive phone app are related learning goals, but they are not the same deliverable.
What this project does—and what it does not
Kaggle’s Digit Recognizer is a classification competition: given rows of pixel values, predict the digit label for each test image and prepare a submission. An Android digit recognizer instead accepts a new input, such as a digit drawn on the screen, preprocesses it, and runs inference inside an app.
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The Kaggle data is a useful starting point for learning image classification, but a competition submission file is not an Android app. The app needs a model packaged in a format the Android code can load, plus input handling that matches the model’s expectations. This article describes a workflow, not a tested implementation; no project-specific score or device result is established.
Inspect Kaggle’s digit data
Kaggle describes each grayscale handwritten digit as a 28×28 image represented by 784 flattened pixel values. The training CSV includes a label column identifying the digit from 0 through 9; the test CSV supplies pixels without labels. See Kaggle’s Digit Recognizer competition overview and competition data page for the dataset and its access conditions.
- Training rows: use the label as the target and the 784 pixel columns as input features.
- Test rows: contain no labels, so they are for generating predictions rather than checking whether your model is correct.
- Image shape: each flat row corresponds to 28 rows by 28 columns. Reshape it consistently if your model expects a two-dimensional image or a channel dimension.
Check Kaggle’s current competition rules before redistributing or republishing its data. Keep the distinction between a local learning dataset and a submission: Kaggle’s competition metric is categorization accuracy, and its expected submission pairs image identifiers with predicted labels. A prediction file for the competition does not accept a user’s drawing or provide interactive inference.
Train and validate before preparing a mobile model
Start with the training CSV and reserve a held-out validation split. Fit preprocessing only on the training portion, apply the same transformations to validation inputs, and measure the model on examples whose labels were not used to fit it. This gives you a local check before adapting the model for Android; the competition’s unlabeled test data cannot provide that check.
- Separate inputs and targets. Use the 784 pixel values as features and the digit label as the target. Preserve row order and identifiers if you later create competition predictions.
- Choose a model input shape. A classifier may consume a flat vector or an image tensor. For an image tensor, reshape each example to 28×28 and include any channel dimension required by the selected model.
- Normalize deliberately. Determine the pixel value range in the CSV, select a normalization appropriate to the model, and apply it identically during training, validation, and app inference.
- Evaluate on the held-out split. Record the split method, preprocessing, model version, and validation result. Do not treat a score as a Kaggle leaderboard result unless you actually submit the required predictions.
- Save the trained model and its preprocessing contract. Document the expected shape, data type, pixel scale, and class order so the Android app can reproduce them.
No accuracy figure is provided here for this project. A result is meaningful only when it is measured on a stated validation set or competition submission and tied to the model and preprocessing that produced it.
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Prepare the model for Android inference
TensorFlow Lite is one supported route for running a model in an Android app. TensorFlow’s Android documentation describes using TensorFlow Lite in Android applications: TensorFlow Lite for Android. TensorFlow and TensorFlow Lite models use different formats and are not interchangeable, so plan for conversion or another compatible export path rather than assuming the training artifact can be loaded directly by the app.
Before wiring the model into a drawing screen, verify that conversion preserves the model’s input and output contract. The Android preprocessing must match training: pixel order, image orientation, scaling, tensor shape, and data type all affect predictions. A mismatch can produce plausible-looking but incorrect output even if the model itself loads successfully.
- Confirm the exported model’s input tensor shape and data type.
- Convert a sample 28×28 input using the same normalization as training and inspect the values passed to inference.
- Confirm the output corresponds to the ten digit classes and that the app interprets their order correctly.
- Run a repeatable set of validation examples through both the original model and the Android inference path, then compare outputs.
These checks are implementation steps, not claims that a particular conversion or Android build has been tested here.
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Use TensorFlow’s Android digit classifier as a reference
TensorFlow’s official examples include an Android digit-classification application trained on MNIST with a drawing interface. Its Digit Classification Demo Application README describes an example workflow using Android Studio and a physical Android device with developer mode enabled. The README lists SDK 23, or Android 6.0, as a minimum for that sample; it is a dated example requirement, not a recommendation for a new phone or a guarantee about current build tooling.
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Where Antigravity CLI fits
Antigravity CLI is an agentic coding assistant that can help inspect and edit project files and work with terminal tasks. Google’s official CLI documentation documents installation for macOS, Linux, and Windows. It does not establish local Android or Termux compatibility, so use a documented desktop or server environment for the CLI rather than assuming it runs on the phone.
From that computer, keep the Android project in a workspace the CLI can access. You can ask it to explain the data pipeline, inspect the model input contract, propose conversion steps, or help identify a build error. Review generated changes before accepting them, and run the training, conversion, and Android build yourself in the appropriate tools. Antigravity’s project workflow is described in its official documentation.
Keep the two outcomes separate when reporting results
A strong project write-up states exactly what was completed: whether the model was trained, which validation split was used, whether a competition submission was made, whether conversion succeeded, and whether an Android app ran on a physical device. If reporting a device result, identify the device, Android version or API level, model version, and measured result. Without those observations, describe the workflow as instructions and the expected behavior as a goal, not as a tested outcome.
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