tasnif 0.2.0

Unsupervised image clustering with modern deep embeddings (timm, CLIP), PCA and K-Means.

Unsupervised image clustering with modern deep embeddings.

tasnif turns a folder of images into clusters you can browse on disk — no labels required. It uses modern pretrained vision backbones (timm by default, optionally CLIP via open_clip), reduces the embedding with PCA, and runs K-Means on top.

Highlights

• Modern backbones — any timm model (ResNet, ConvNeXt, ViT, DINOv2, ...) or CLIP via the [clip] extra.
• GPU / MPS / CPU with automatic device detection.
• scikit-learn-style API: fit, predict, fit_predict, transform.
• Rich export: per-cluster folders, CSV manifest, JSON summary, preview grids, raw embeddings.
• Multiple materialization modes: copy, symlink, move, or none (metadata only).
• First-class CLI powered by Typer.
• Pluggable embedders — register your own backend.
• Deterministic with a random_state seed.

Installation

tasnif is built and packaged with uv, but plain pip works too.

pip install tasnif                # core
pip install "tasnif[cli]"         # core + CLI
pip install "tasnif[clip]"        # core + CLIP backend
pip install "tasnif[all]"         # everything

Development setup:

git clone https://github.com/cobanov/tasnif
cd tasnif
uv sync --extra dev
uv run pytest

Quickstart — Python API

from tasnif import TasnifClusterer

clf = TasnifClusterer(n_clusters=5, embedder="timm", pca_dim=16)
clf.fit("photos/")
result = clf.result_

# Inspect without exporting
print(result.counts)            # e.g. [42, 31, 28, 19, 7]
print(result.silhouette)        # None unless compute_silhouette=True
mapping = result.as_dict()      # {Path('photos/a.jpg'): 2, ...}
buckets = result.by_cluster()   # {0: [Path(...), ...], 1: [...]}

# Export to disk
clf.export("output/", mode="copy")

One-shot helper

from tasnif import cluster_directory

cluster_directory("photos/", "output/", n_clusters=5, mode="symlink")

CLI

# Cluster a directory with the default ResNet-50 (timm) backbone
tasnif cluster photos/ -k 5 -o output/

# Use CLIP and copy via symlink (fast, non-destructive)
tasnif cluster photos/ -k 8 --embedder clip --model ViT-B-32 --mode symlink

# Just compute embeddings, save .npy + .json
tasnif embed photos/ --embedder timm --model convnext_base -o embeddings.npy

# List available backends
tasnif backends

Run tasnif --help to see all options.

Use a different model

Default is timm:resnet50. Pass any timm model name:

clf = TasnifClusterer(
    n_clusters=8,
    embedder="timm",
    embedder_kwargs={"model": "vit_base_patch14_dinov2.lvd142m", "device": "cuda"},
)

CLIP:

clf = TasnifClusterer(
    n_clusters=8,
    embedder="clip",
    embedder_kwargs={"model": "ViT-L-14", "pretrained": "laion2b_s32b_b82k"},
)

Custom backend

Anything implementing the Embedder protocol works:

import numpy as np
from tasnif import TasnifClusterer, register_embedder

class MyEncoder:
    name = "my-encoder"
    @property
    def dim(self): return 128
    @property
    def device(self): return "cpu"
    def embed(self, images, *, batch_size=32, show_progress=True):
        return np.stack([extract(img) for img in images])

register_embedder("my-encoder", lambda: MyEncoder())

clf = TasnifClusterer(n_clusters=5, embedder="my-encoder")

Building blocks

All pieces are independently usable:

from tasnif import (
    discover_images, create_embedder,
    reduce_pca, fit_kmeans, export_clusters, ClusterResult,
)

paths = discover_images("photos/")
embedder = create_embedder("timm", model="resnet50")
embeddings = embedder.embed([open_pil(p) for p in paths])
reduced = reduce_pca(embeddings, n_components=16)
fit = fit_kmeans(reduced, n_clusters=5, compute_silhouette=True)

result = ClusterResult(
    labels=fit.labels, centroids=fit.centroids, counts=fit.counts,
    paths=tuple(paths), n_clusters=5, inertia=fit.inertia, silhouette=fit.silhouette,
    embedder=embedder.name, pca_dim=16,
)
export_clusters(result, "output/")

Migrating from 0.1.x

The 0.1.x API (Tasnif().read().calculate().export()) was removed in 0.2.0. Replace with:

- from tasnif import Tasnif
- c = Tasnif(num_classes=5, pca_dim=16, use_gpu=False)
- c.read("photos/")
- c.calculate()
- c.export("output/")
+ from tasnif import TasnifClusterer
+ clf = TasnifClusterer(n_clusters=5, pca_dim=16, embedder_kwargs={"device": "auto"})
+ clf.fit("photos/")
+ clf.export("output/")

See CHANGELOG.md for the full list of breaking changes.

Contributing

Issues and PRs welcome. Before submitting, run:

uv run ruff check . && uv run ruff format --check .
uv run mypy
uv run pytest

Pre-commit is configured — install hooks with uv run pre-commit install.

License

MIT — see LICENSE.