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Recent Releases of Zamba

Zamba - v2.7.2

  • Fix single-GPU image training wrongly running under DDP when devices="auto".
  • Compile only the image backbone, not the whole module, so torch.compile no longer breaks training under DDP.
  • Reconcile _orig_mod. checkpoint keys so compiled-model checkpoints load on any setup.

Biosphere - Terrestrial Wildlife - Python
Published by pjbull 27 days ago

Zamba - v2.7.1

  • Fix video and image training on PyTorch 2.7+ by dropping deprecated scheduler kwargs like verbose.
  • Fix image training LR finder crash by deferring torch.compile until after tuning.
  • Set torch-backend = "auto" in [tool.uv] for clone-based uv installs.
  • Document uv install tips: --torch-backend=auto and protobuf/setuptools overrides for zamba[video,image].

Biosphere - Terrestrial Wildlife - Python
Published by pjbull 28 days ago

Zamba - 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.

Biosphere - Terrestrial Wildlife - Python
Published by pjbull 28 days ago

Zamba - v2.6.1

  • Fix bug that prevented loading checkpoints when model name was provided (PR #359)

Biosphere - Terrestrial Wildlife - Python
Published by pjbull over 1 year ago

Zamba - v2.6.0

  • Added support for training and classifying on images (PR #349) 🖼️ ✨

Biosphere - Terrestrial Wildlife - Python
Published by pjbull over 1 year ago

Zamba - v2.5.0

Note: This version removes support for python 3.8 and 3.9.

What's Changed

New Contributors

Full Changelog: https://github.com/drivendataorg/zamba/compare/v2.4.1...v2.5.0

Biosphere - Terrestrial Wildlife - Python
Published by ejm714 almost 2 years ago

Zamba - v2.4.1

Bug fixes for docs

Biosphere - Terrestrial Wildlife - Python
Published by pjbull over 2 years ago

Zamba - v2.4.0

  • Adds experimental image support (PR #314)
  • Clarifies installation instructions for Linux and Windows operating systems (PR #299)

Biosphere - Terrestrial Wildlife - Python
Published by ejm714 over 2 years ago

Zamba - v2.3.2

  • Pin Pydantic to less than v2.0 (PR #277)

Biosphere - Terrestrial Wildlife - Python
Published by ejm714 about 3 years ago

Zamba - v2.3.1

Biosphere - Terrestrial Wildlife - Python
Published by ejm714 about 3 years ago

Zamba - v2.3.0

Model release

Biosphere - Terrestrial Wildlife - Python
Published by ejm714 over 3 years ago

Zamba - v2.2.4

  • Do not cache videos if the VIDEO_CACHE_DIR environment variable is an empty string or zero (PR #245)

Biosphere - Terrestrial Wildlife - Python
Published by ejm714 over 3 years ago

Zamba - v2.2.3

  • Fixes Lightning deprecation of DDPPlugin (PR #244)

Biosphere - Terrestrial Wildlife - Python
Published by ejm714 over 3 years ago

Zamba - v2.2.2

  • Adds a page to the docs summarizing the performance of the African species classification model on a holdout set (PR #235)

Biosphere - Terrestrial Wildlife - Python
Published by ejm714 almost 4 years ago

Zamba - v2.2.1

  • Turn off showing local variables in Typer's exception and error handling (PR #237)
  • Fixes bug where the column order was incorrect for training models when the provided labels are a subset of the model's default labels (PR #236)

Biosphere - Terrestrial Wildlife - Python
Published by ejm714 almost 4 years ago

Zamba - v2.2.0

Model releases and new features

  • The default time_distributed model (African species classification) has been retrained on over 250,000 videos. This 16x increase in training data significantly improves accuracy. This new version replaces the previous one. (PR #226, PR #232)
  • A new default model option is added: blank_nonblank. This model only does blank detection. This binary model can be trained and finetuned in the same way as the species classification models. This model was trained on both African and European data, totaling over 263,000 training videos. (PR #228)
  • Detect if a user is training in a binary model and preprocess the labels accordingly (PR #215)

Bug fixes and improvements

  • Add a validator to ensure that using a model’s default labels is only possible when the species in the provided labels file are a subset of those (PR #229)
  • Refactor the logic in instantiate_model for clarity (PR #229)
  • Use pqdm to check for missing files in parallel (PR #224)
  • Set model_name based on the provided checkpoint so that user-trained models use the appropriate video loader config (PR #221)
  • Leave data_dir as a relative path (PR #219)
  • Ensure hparams yaml files get included in the source distribution (PR #210)
  • Hold back setuptools so mkdocstrings works (PR #207)
  • Factor out get_cached_array_path (PR #202)

Biosphere - Terrestrial Wildlife - Python
Published by ejm714 almost 4 years ago

Zamba - v2.1.0

  • Retrains the time distributed species classification model using the updated MegadetectorLite frame selection (PR #199)
  • Replaces the MegadetectorLite frame selection model with an improved model trained on significantly more data (PR #195)

Biosphere - Terrestrial Wildlife - Python
Published by ejm714 about 4 years ago

Zamba - v2.0.4

  • Pins thop to an earlier version (PR #191)
  • Fixes caching so a previously downloaded checkpoint file actually gets used (PR #190, PR #194)
  • Removes a lightning deprecation warning for DDP (PR #187)
  • Ignores extra columns in the user-provided labels or filepaths csv (PR #186)

Biosphere - Terrestrial Wildlife - Python
Published by ejm714 about 4 years ago

Zamba - v2.0.3

Releasing to pick up #179.

  • PR #179 removes the DensePose extra from the default dev requirements and tests. Docs are updated to clarify how to install and run tests for DensePose.

Biosphere - Terrestrial Wildlife - Python
Published by ejm714 about 4 years ago

Zamba - v2.0.2

Releasing to pick up #172.

  • PR #172 fixes bug where video loading that uses the YoloX model (all of the built in models) resulted in videos not being able to load.

Biosphere - Terrestrial Wildlife - Python
Published by pjbull over 4 years ago

Zamba - v2.0.1

Releasing to pick up #167 and #169.

  • PR #169 fixes error in splitting data into train/test/val when only a few videos.
  • PR #167 refactors yolox into an object_detection module

Other documentation fixes also included.

Biosphere - Terrestrial Wildlife - Python
Published by pjbull over 4 years ago

Zamba - v2.0.0

Releases new version v2 of the zamba package including:

  • New and improved models
  • Additional species and habitats
  • Model finetuning and training

Previous model: Machine learning competition

The algorithms used by zamba v1 were based on the winning solution from the Pri-matrix Factorization machine learning competition, hosted by DrivenData. Data for the competition was provided by the Chimp&See project and manually labeled by volunteers. The competition had over 300 participants and over 450 submissions throughout the three month challenge. The v1 algorithm was adapted from the winning competition submission, with some aspects changed during development to improve performance.

The core algorithm in zamba v1 was a stacked ensemble which consisted of a first layer of models that were then combined into a final prediction in a second layer. The first level of the stack consisted of 5 keras deep learning models, whose individual predictions were combined in the second level of the stack to form the final prediction.

In v2, the stacked ensemble algorithm from v1 is replaced with three more powerful single-model options: time_distributed, slowfast, and european. The new models utilize state-of-the-art image and video classification architectures, and are able to outperform the much more computationally intensive stacked ensemble model.

New geographies and species

zamba v2 incorporates data from western Europe (Germany). The new data is packaged in the pretrained european model, which can predict 11 common European species not present in zamba v1.

zamba v2 also incorporates new training data from 15 countries in central and west Africa, and adds 12 additional species to the pretrained African models.

Retraining flexibility

Model training is made available zamba v2, so users can finetune a pretrained model using their own data to improve performance for a specific ecology or set of sites. zamba v2 also allows users to retrain a model on completely new species labels.

Biosphere - Terrestrial Wildlife - Python
Published by pjbull over 4 years ago