[0.9.0] - 2026-08-23

Fixed

• PSNR.similarity_score accepts a channel-less grayscale image again: a 2-D
  array passed with the default dims="HWC" raised instead of being read as
  single-channel, unlike SSIM and the rest of the library.
• make test-types no longer prints a DeprecationWarning: the
  numpy.typing.mypy_plugin entry is removed from the mypy configuration.
• The development interpreter is pinned to Python 3.10 (.python-version), the
  project's minimum supported version and the one every CI job already uses.
• The ruff check CI step no longer fails on import sorting (I001) in
  tests/neural_networks/test_oxford_flowers_quick.py and
  test_oxford_flowers_slow.py.

Added

• load_from_disk now forwards keyword arguments to from_dict, so an
  embedder can be handed the objects its file cannot hold. The Siamese
  networks use it for their transform:
  ContrastiveSiameseNetwork.load_from_disk(path, transform=transform)
  restores the exact embeddings of a network built with a custom one.
• The embedders of pyvisim.neural_networks (ClipEmbedder,
  ContrastiveSiameseNetwork, BCESiameseNetwork) are now serialisable to a
  safetensors .embedder file via save_to_disk/load_from_disk, weights
  included; a reloaded embedder produces identical embeddings without downloading
  any pretrained weights.
• NeuralImageEmbedder (in pyvisim.neural_networks): the shared base for the
  neural embedders, both a SerializableImageEmbedder and a torch.nn.Module.
  SiameseNetworkBase now derives from it, so the Siamese networks and the
  classic embedders expose the same embed/similarity_score surface.
• Clustering models can now be built from a fitted scikit-learn estimator:
  KMeans.from_sklearn, DiagCovarGaussianMixture.from_sklearn and
  PCA.from_sklearn, plus load_clustering_model_from_sklearn on VLADEncoder
  and FisherVectorEncoder to drop one straight into an encoder. Handy for reusing
  a vocabulary you already trained with scikit-learn.

Changed

• The Siamese networks now store the repr of their transform in the .embedder
  file and warn on load_from_disk when the rebuilt network's transform differs
  from it, instead of warning on every save of a custom transform.
• tqdm and requests are no longer runtime dependencies of the core
  package; they moved into the nn extra. Only pyvisim.datasets uses them,
  and that module already requires torch from the same extra.
• CI restores the Oxford Flowers dataset and the pretrained backbone weights from
  the GitHub Actions cache instead of re-downloading them on every run; the new
  Warm asset cache workflow keeps that cache populated on main.
• The similarity_func registry in pyvisim._utils now maps the metric names
  straight onto pyvisim.distance.
• ℹ️ Dropped scikit-learn as a runtime dependency.
• Added PSNR (under pyvisim.pixelwise) and SSIM/MSSSIM (under
  pyvisim.structural) metrics as well as their benchmark scripts against
  existing implementations under docs/pixelwise/benchmarks and
  docs/structural/benchmarks.
• BCESiameseNetwork (in pyvisim.neural_networks): the pair-classifying
  Siamese variant of Koch, Zemel & Salakhutdinov (2015).
• The Siamese networks are split along a shared abstract base,
  SiameseNetworkBase.
• Removed the Siamese Network's train scripts. This is now demonstrated
  in a notebook in the "examples" repository.

Breaking

• ⚠️ VLADEmbedder, FisherVectorEmbedder and Pipeline moved from
  pyvisim.encoders to pyvisim.classic.

• ⚠️ The bundled pretrained VLAD and Fisher Vector encoders are removed to make the binary smaller, together
  with from_pretrained, PretrainedVLAD/PretrainedFisher, the deprecated
  weights= argument and KMeansWeights/GMMWeights. Train a vocabulary with
  learn() and persist it with save_to_disk/load_from_disk instead.

• ⚠️ "Encoder" is now "embedder" throughout: ImageEncoderBase -> ImageEmbedderBase,
  VLADEncoder -> VLADEmbedder, FisherVectorEncoder -> FisherVectorEmbedder,
  the Encoder protocol -> Embedder, encode() -> embed() and store.encoder
  -> store.embedder.

• ⚠️ Saved models use the .embedder suffix and an embedder_class state key, so
  existing .encoder files no longer load. Re-save them with save_to_disk.

• ⚠️ SiameseNeuralNetwork is renamed to ContrastiveSiameseNetwork
  (pyvisim.neural_networks.siamese.siamese_neural_network is gone; the base
  class now lives in pyvisim.neural_networks.siamese._base_siamese):

  from pyvisim.neural_networks import ContrastiveSiameseNetwork
  
  model = ContrastiveSiameseNetwork(backbone="resnet18", embedding_dim=128)
  score = model.similarity_score(image1, image2)  # cosine similarity in [-1, 1]

• ⚠️ The clustering models (KMeans, DiagCovarGaussianMixture, PCA,
  ClusteringModelBase) are now internal to the encoders package and moved from
  pyvisim.clustering to pyvisim.classic._clustering.

• ⚠️ Encoder clustering parameters changed: pass rng instead of random_state
  inside kmeans_params / gmm_params / pca_params (see
  vlad.md and
  fisher_vector.md for every accepted key).