v2.7.0

• Add the speciesnet image classification model, a zamba-compatible conversion of Google's SpeciesNet classifier (EfficientNetV2-M backbone, 2,000+ class global taxonomy). Select it with zamba image predict --model speciesnet or zamba image train --model speciesnet. See the Available Models page.
• Persist the preprocessing model_family on image classifier checkpoints and derive inference transforms (resize size, interpolation, normalization) from the loaded checkpoint rather than from the model_name string. This fixes incorrect preprocessing (and near-random predictions) when running zamba image predict --checkpoint <ckpt> on a fine-tuned SpeciesNet model without also passing --model.
• Split core dependencies into optional extras: video (av, ffmpeg-python, pytorchvideo, pixeltable-yolox, etc.), image (megadetector, Pillow), tests (pytest, black, flake8, coverage, nvidia-ml-py, etc.), and docs (mkdocs, mike, mkdocstrings). Base install no longer pulls in video/image stacks; use pip install zamba[video], zamba[image], or zamba[video,image].
• Remove requirements-dev.txt and requirements-dev/; use uv pip install -e ".[image,video]" --group dev for development or pip install -e ".[tests,image,video,docs]" with pip.
• Replace mlflow with mlflow-skinny in core dependencies. Add pyarrow>=23.0.0. Add Windows-specific torch version constraint for gloo bug. Declare Python 3.11–3.13 support and requires-python = ">=3.11, <3.14".
• Remove DensePose from optional dependencies so the package is publishable to PyPI. DensePose (detectron2, detectron2-densepose) must be installed from GitHub; see docs. Running zamba densepose without them prints install instructions and exits. Add [tool.uv.extra-build-dependencies] for detectron2 (torch) so CI can build it from GitHub.
• Pin setuptools<82 so the image extra works on Python 3.12 (megadetector’s yolov5 stack requires pkg_resources, which setuptools 82 removed). Add [tool.uv] override-dependencies for protobuf and setuptools.
• Add zamba.models.config_common with shared types and validators (ModelEnum, MonitorEnum, ZambaBaseModel, RegionEnum, get_filepaths, validate_model_cache_dir, etc.). Move video-specific file/checkpoint logic into zamba.models.config and reuse common helpers.
• Add zamba.models.instantiation and move instantiate_model, head-replacement, and resume logic out of model_manager for clearer separation.
• Add lazy model registration in zamba.models.registry (ensure_registered()): video model classes are imported only when needed so the package can be imported without video dependencies. Config validation calls ensure_registered() when resolving checkpoint/model name.
• Register image and utils sub-apps lazily so zamba --help and non-image/non-utils commands work without image or densepose dependencies. Defer imports of VideoLoaderConfig and config classes into the train, predict, and depth command callbacks.
• Handle missing DensePose dependencies in the densepose command: catch ImportError when running the model and print install-from-GitHub instructions and exit with code 1.
• Make NPY cache path hashing stable: use a JSON-serializable, order-invariant representation of the config (including Enum and Path) instead of str(hashed_part). For local absolute paths, use a relative-style path in the cache key.
• Fix cache cleanup in npy_cache.__del__ by comparing Path(cache_path).parents[0] with Path(tempfile.gettempdir()).
• Import image classifier lazily from zamba.images to avoid pulling in torch at package import time. Images config and manager use zamba.models.config_common and zamba.models.instantiation instead of config/utils from video.
• Make megadetector import resilient: try megadetector.detection.run_detector, fall back to detection.run_detector. Make MLflow optional in image training (log warning and continue without it if import or setup fails). Disable torch.compile on Windows in addition to macOS.
• Add pytest markers video and image and skip test files when the corresponding extra is not installed (conftest collect_ignore based on _HAS_VIDEO / _HAS_IMAGE). Define video-only fixtures and the dummy video model only when video deps are present. Set CUDA_VISIBLE_DEVICES=0 in conftest to avoid DDP issues under pytest-xdist.
• Add Makefile targets test-fast (fail on first failure) and test-image-only / test-video-only (isolated venvs with only image or video extra). CI runs these isolation steps and installs image,video extras for the main test matrix. DensePose tests install detectron2 from GitHub with --no-build-isolation instead of using a densepose extra.
• Update tests to use new config/instantiation imports and markers; add image dataset import check in the install smoke test.
• Installation docs: document optional extras (video, image), PyPI install (pip install zamba[video] etc.), and Windows (image extra no extra tools; video needs Visual Studio Build Tools and FFmpeg). State FFmpeg only required for video workflows and recommend FFmpeg 4.x. Contribute page: dev install with --group dev, DensePose deps from GitHub, make requirements with uv. DensePose doc: install detectron2/detectron2-densepose from GitHub; note that zamba densepose prints instructions if deps missing.
• Re-enable PyPI publish steps in the release workflow (Test PyPI and Production PyPI). Docs workflows and release job install .[docs] instead of requirements-dev/docs.txt.
• Add deterministic option (default False) to video and image prediction configs. Inference always seeds RNGs (INFERENCE_SEED, default 55); set deterministic=True for strict CUDA/cuDNN reproducibility at some GPU throughput cost.
• Fix non-deterministic frame ordering in MegadetectorLite score_sorted fill mode when frame scores tie; equal scores now break by lowest frame index.