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A Siamese network compares images by mapping each one to an embedding vector, then measuring how far apart the vectors are. With triplet loss, training encourages an anchor image to be closer to a related positive image than to an unrelated negative image by at least a chosen margin. The result is a useful similarity signal for retrieval or matching—not, by itself, a calibrated probability that two images match.
What image similarity means in a Siamese network
“Similar” has to describe a specific visual relationship before the model can learn it. Depending on the task, two images might be similar because they show the same object, the same product from different angles, a near-duplicate, or the same person. These are different targets, so the labels and evaluation data must reflect the relationship the application actually needs.
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A Siamese design applies the same embedding function to multiple inputs. If that function is f, an image becomes a vector such as f(image). Sharing the embedding model means all images are represented in the same learned space. FaceNet introduced this broader embedding-space approach for face recognition and clustering; the Keras example adapts it to general image similarity (FaceNet paper; Keras image-similarity example).
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The anchor, positive, and negative
Each training example contains three images:
- Anchor: the reference image.
- Positive: an image that should be considered similar to the anchor under the task definition.
- Negative: an image that should be considered dissimilar, ideally a useful contrast for the task.
The shared network maps all three images to vectors. Training then compares the squared Euclidean distance from anchor to positive with the distance from anchor to negative. The Keras example defines its loss as:
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L(A, P, N) = max(||f(A) - f(P)||² - ||f(A) - f(N)||² + margin, 0)
When the negative is farther from the anchor than the positive by at least the margin, the triplet contributes zero loss. Otherwise, the loss penalizes the embedding arrangement. The example uses a margin of 0.5; that is an example setting, not a value established as best for other datasets or distance conventions.
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Why triplet construction matters
The loss can only enforce the relationships encoded by the labels. If a positive pair is not genuinely similar for the intended use, or a negative is actually a valid match, optimization teaches the wrong visual rule. Sampling also shapes what the model learns: easy negatives may provide little useful distinction, while confusing or mislabeled examples can make training misleading. The Keras tutorial demonstrates preparing filename-based triplets for the Totally Looks Like dataset; its sampling choices should not be assumed to fit another application.
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What the Keras example implements
Input pipeline and preprocessing
The tutorial builds a tf.data pipeline that reads JPEG files, decodes them as three-channel images, converts pixel values to floating point, resizes images to 200 × 200 pixels, and batches triplets. Those input dimensions and the dataset-specific triplet preparation are properties of the worked example. For another dataset, select image decoding, normalization, resizing, and sampling to match the data and intended visual relation.
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Shared embedding model
The example starts from an ImageNet-pretrained ResNet50 without its classification head. It flattens the backbone features, adds dense layers and batch normalization, and produces a 256-dimensional embedding. Early ResNet layers are frozen while later layers are trainable. The same embedding-model object is applied to the anchor, positive, and negative inputs, ensuring that all three vectors use shared weights. This is one transfer-learning configuration to experiment with, not a universal architecture or freeze boundary.
Custom training loop
Because each training item contains three inputs and uses a custom loss, the example wraps the network in a custom Keras Model. Its train_step and test_step methods use tf.GradientTape to calculate gradients and apply them with the configured optimizer, while tracking mean loss. The tutorial was created and last modified on 2021-03-25, so check its code against the Keras and TensorFlow versions in your environment, including current API and model-serialization behavior, before adopting it unchanged (Keras example).
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Using embeddings to compare images
After training, apply the embedding function to images and compare their vectors. The tutorial demonstrates cosine similarity for sample embeddings. Cosine similarity generally increases as vector directions align; squared Euclidean distance decreases as vectors become closer. These are distinct comparison conventions, and the convention used at inference should be the one evaluated for the application.
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How to choose and validate the implementation
There is no universally best setting established by the cited tutorial or FaceNet paper for a different image dataset. Treat the following as coupled design decisions, and compare candidates on held-out data from the target use case:
Quick Recap
- Pair definition and labels: specify the relationship a positive pair must share and what makes a negative relevant.
- Triplet sampling: assess whether selected positives and negatives provide informative, correctly labeled contrasts.
- Backbone and initialization: test whether a pretrained feature extractor is suitable for the image domain rather than assuming the example’s ResNet50 is optimal.
- Trainable layers: compare freezing and fine-tuning choices on the target data.
- Distance and margin: align the loss distance, margin, and downstream comparison method; validate rather than copying the example’s squared distance or 0.5 margin by default.
- Evaluation: use held-out retrieval evaluation for search, or held-out verification examples to select and assess a match threshold.
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