Zamba

monkey-vid.mp4

Zamba means "forest" in Lingala, a Bantu language spoken throughout the Democratic Republic of the Congo and the Republic of the Congo.

zamba is a tool built in Python that uses machine learning and computer vision to automatically detect and classify animals in camera trap videos. You can use zamba to:

Identify which species appear in each video
Filter out blank videos
Create your own custom models that identify your species in your habitats
Estimate the distance between animals in the frame and the camera
And more! 🙈 🙉 🙊

The official models in zamba can identify blank videos (where no animal is present) along with 32 species common to Africa and 11 species common to Europe. Users can also finetune models using their own labeled videos to then make predictions for new species and/or new ecologies.

zamba can be used both as a command-line tool and as a Python package. It is also available as a user-friendly website application, Zamba Cloud.

We encourage people to share their custom models trained with Zamba. If you train a model and want to make it available, please add it to the Model Zoo Wiki for others to be able to use!

Visit https://zamba.drivendata.org/docs/ for full documentation and tutorials.

Installing `zamba`

First, make sure you have the prerequisites installed:

Python 3.11 or 3.12
FFmpeg > 4.3

Then run:

pip install https://github.com/drivendataorg/zamba/releases/latest/download/zamba.tar.gz

See the Installation page of the documentation for details.

Getting started

Once you have zamba installed, some good starting points are:

The Quickstart page for basic examples of usage
The user tutorial for either classifying videos or training a model depending on what you want to do with zamba

Example usage

Once zamba is installed, you can see the basic command options with:

$ zamba --help

 Usage: zamba [OPTIONS] COMMAND [ARGS]...

 Zamba is a tool built in Python to automatically identify the species seen in camera trap
 videos from sites in Africa and Europe. Visit https://zamba.drivendata.org/docs for more
 in-depth documentation.

╭─ Options ─────────────────────────────────────────────────────────────────────────────────╮
│ --version                     Show zamba version and exit.                                │
│ --install-completion          Install completion for the current shell.                   │
│ --show-completion             Show completion for the current shell, to copy it or        │
│                               customize the installation.                                 │
│ --help                        Show this message and exit.                                 │
╰───────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Commands ────────────────────────────────────────────────────────────────────────────────╮
│ densepose      Run densepose algorithm on videos.                                         │
│ depth          Estimate animal distance at each second in the video.                      │
│ predict        Identify species in a video.                                               │
│ train          Train a model on your labeled data.                                        │
╰───────────────────────────────────────────────────────────────────────────────────────────╯

zamba can be used "out of the box" to generate predictions or train a model using your own videos. zamba supports the same video formats as FFmpeg, which are listed here. Any videos that fail a set of FFmpeg checks will be skipped during inference or training.

Classifying unlabeled videos

$ zamba predict --data-dir path/to/videos

By default, predictions will be saved to zamba_predictions.csv. Run zamba predict --help to list all possible options to pass to predict.

See the Quickstart page or the user tutorial on classifying videos for more details.

Training a model

$ zamba train --data-dir path/to/videos --labels path_to_labels.csv --save_dir my_trained_model

The newly trained model will be saved to the specified save directory. The folder will contain a model checkpoint as well as training configuration, model hyperparameters, and validation and test metrics. Run zamba train --help to list all possible options to pass to train.

You can use your trained model on new videos by editing the train_configuration.yaml that is generated by zamba. Add a predict_config section to the yaml that points to the checkpoint file that is generated:

...
# generated train_config and video_loader_config
...

predict_config:
  checkpoint: PATH_TO_YOUR_CHECKPOINT_FILE

Now you can pass this configuration to the command line. See the Quickstart page or the user tutorial on training a model for more details.

You can then share your model with others by adding it to the Model Zoo Wiki.

Estimating distance between animals and the camera

$ zamba depth --data-dir path/to/videos

By default, predictions will be saved to depth_predictions.csv. Run zamba depth --help to list all possible options to pass to depth.

See the depth estimation page for more details.

Contributing

We would love your contributions of code fixes, new models, additional training data, docs revisions, and anything else you can bring to the project!

See the docs page on contributing to zamba for details.

drivendataorg/zamba