photoprism/internal/ai/classify

PhotoPrism — Classification Package

Last Updated: December 23, 2025

Overview

internal/ai/classify wraps PhotoPrism’s TensorFlow-based image classification (labels). It loads SavedModel classifiers (Nasnet by default), prepares inputs, runs inference, and maps output probabilities to label rules.

How It Works

• Model Loading — The classifier loads a SavedModel under assets/models/<name> and resolves model tags and input/output ops (see vision.yml overrides for custom models).
• Input Preparation — JPEGs are decoded and resized/cropped to the model’s expected input resolution.
• Inference — The model outputs probabilities; Rules apply thresholds and priority to produce final labels.

Memory & Performance

TensorFlow tensors allocate C memory and are freed by Go GC finalizers. To keep RSS bounded during long runs, PhotoPrism periodically triggers garbage collection to return freed tensor memory to the OS. Tune with:

• PHOTOPRISM_TF_GC_EVERY (default 200, 0 disables).
  Lower values reduce peak RSS but increase GC overhead and can slow indexing.

Troubleshooting Tips

• Labels are empty: Verify the model labels file and that Rules thresholds are not too strict.
• Model load failures: Ensure saved_model.pb and variables/ exist under the configured model path.
• Unexpected outputs: Check TensorFlow.Input/Output settings in vision.yml for custom models.

Related Docs

• internal/ai/vision/README.md — model registry and vision.yml configuration
• internal/ai/tensorflow/README.md — TensorFlow helpers, GC behavior, and model loading