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1. Organize and inspect labeled images
Each image needs a reliable class label. With tf.keras.utils.image_dataset_from_directory, a folder-per-class layout lets the directory names supply those labels. Before training, inspect representative images and the generated class names to catch mislabeled, unreadable, or unexpectedly grouped files.
TensorFlow’s flower categories are tutorial examples, not a recommended label set for other projects. Check that your own dataset’s labels are accurate and that you have the right to use its images; the tutorial’s licensing information for its sample images does not establish rights for your data. See TensorFlow’s image-classification tutorial and image-loading tutorial.
2. Split data for training, validation, and testing
Use training images to update model weights, validation images to monitor training and guide development choices, and a separate test set for a final evaluation after those choices are made. Reusing test results to repeatedly tune the model weakens the independence of that final check.
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TensorFlow examples illustrate different splits: its flower image-classification tutorial uses 80% for training and 20% for validation, while its TensorFlow Datasets example uses 80% training, 10% validation, and 10% test. These are example recipes, not universal proportions. Choose a split that leaves enough representative examples in every class for the evaluation you need. The directory tutorial demonstrates training and validation data; a separate test set remains important for a final check.
3. Load images and build the input pipeline
For images arranged in class folders, tf.keras.utils.image_dataset_from_directory can create batched tf.data.Dataset objects. Set image dimensions and batch size for your model and available memory rather than copying tutorial values without considering the task.
TensorFlow’s directory-loading example shows a batch of 32 RGB images at 180 × 180 pixels, with a corresponding batch of 32 labels. Those shapes describe the tutorial configuration, not a required input size or batch size. The image-loading tutorial covers this workflow. Use tf.data directly when you need more control, or explore TensorFlow’s computer-vision overview for other dataset options.
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For a more efficient pipeline, cache data only when the dataset and available storage permit it, and use prefetching to overlap input work with model execution. Those steps can help keep data ready for training, but caching a dataset that exceeds available memory or storage is not a suitable default.
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Preprocessing is part of the model’s input contract. TensorFlow’s basic flower example starts with RGB values in the range [0, 255] and uses Rescaling(1./255) to map them to [0, 1]. Its MobileNetV2 transfer-learning example instead uses MobileNetV2’s preprocessing function, which maps inputs to [-1, 1]. These transformations are not interchangeable: check the chosen architecture’s requirements.
When practical, include preprocessing in the model so training and inference use the same transformation. If preprocessing happens elsewhere in the serving pipeline, verify that it matches what the model saw during training. TensorFlow documents the basic approach in its classification tutorial and MobileNetV2 approach in its transfer-learning tutorial.
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5. Train a baseline CNN
A small CNN makes the training workflow concrete: convolution and pooling blocks learn image features, followed by dense layers that produce class predictions. TensorFlow’s image-loading tutorial demonstrates a sequential model with three convolution blocks, each followed by max pooling, then a 128-unit ReLU dense layer and an output layer sized for the number of classes. It compiles the model with Adam and sparse categorical cross-entropy configured for logits, then trains with Model.fit and validation data.
That architecture is an instructional example, not a tuned recommendation. Its tutorial run should not be treated as a production design or as evidence of expected performance. For TensorFlow’s full code and explanation, see Load and preprocess images.
6. Monitor validation behavior and address overfitting
Track training and validation loss and accuracy during development. If training performance improves while validation performance stalls or worsens, the gap can indicate overfitting: the model is fitting its training examples better than it generalizes to unseen data.
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In TensorFlow’s flower-classification tutorial, training accuracy rises while validation accuracy stalls around 60%; TensorFlow identifies that gap as a possible sign of overfitting. That is an observed result in that example, not an expected accuracy for your dataset. The tutorial demonstrates random image augmentation and dropout as ways to mitigate overfitting, while its transfer-learning example uses realistic training-time flips and rotations. These are options to evaluate, not guaranteed fixes. See the classification tutorial and transfer-learning tutorial.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Decide whether to use transfer learning
Transfer learning starts with a model trained on another dataset, removes its original classification head, and adds a new head for your classes. TensorFlow’s example uses MobileNetV2 with ImageNet weights. The tutorial describes ImageNet as containing 1.4 million images across 1,000 classes; those figures describe that dataset, not the size or coverage of your own training data.
TensorFlow outlines two approaches. With feature extraction, freeze the pretrained base and train the new classification head. With fine-tuning, unfreeze selected upper layers of the base and train them along with the new head. In the tutorial’s example, when fine-tuning a base model with BatchNormalization layers, it keeps the base in inference mode to avoid damaging learned non-trainable weights. Follow the chosen model’s guidance for this detail. The transfer-learning tutorial provides the example workflow.
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| Consideration | Training from scratch | Transfer learning |
|---|---|---|
| Data | Suitability depends on the amount and diversity of labeled data available for the task. | A pretrained base can be useful when training a model from scratch is unsuitable for the available data; results depend on task and data. |
| Compute and time | Training the full model requires learning its features from your data. | Feature extraction freezes the pretrained base; fine-tuning trains selected upper layers as well. Actual time and compute depend on the model and setup. |
| Preprocessing and input size | Choose and apply preprocessing for the architecture you build. | Use the pretrained architecture’s expected input size and preprocessing; the MobileNetV2 example maps inputs to [-1, 1]. |
| How to choose | Compare validation behavior during development and final results on the held-out test data. | Compare validation behavior during development and final results on the same held-out test data. The cited tutorials do not provide a controlled head-to-head benchmark or a universal winner. |
8. Evaluate and optionally export the model
After model decisions are complete, evaluate on the reserved test data, which was not used to fit weights or guide tuning. Review class-level errors as well as aggregate metrics: a single overall score can conceal poor performance on a particular class.
Converting to TensorFlow Lite is an optional delivery step when the target is on-device inference, such as mobile, embedded, or IoT use. TensorFlow’s classification tutorial demonstrates saving a model, converting it, and invoking it with the Lite interpreter. After conversion, check that preprocessing and predictions remain consistent with the original model. Conversion is not required to train a classifier.
Check current TensorFlow guidance
The TensorFlow tutorials cited here were last updated in 2024 where update dates are stated, and APIs or package compatibility can change. Before running a project, check TensorFlow’s current installation and API documentation for the TensorFlow, Keras, Python, and accelerator setup you intend to use. The example architectures, batch sizes, epochs, and tutorial results are educational settings, not guarantees for another dataset.
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