This section assumes you have successfully installed
zamba and are ready to train a model or identify species in your videos!
zamba can be used "out of the box" to generate predictions or train a model using your own videos. To perform inference, you simply need to run
zamba predict followed by a set of arguments that let zamba know where your videos are located, which model you want to use, and where to save your output. To train a model, you can similarly run
zamba train and specify your labels. The following sections provide details about these separate modules.
There are two ways to interact with the
zambaas a command line interface tool. This page provides an overview of how to use the CLI.
zambain Python and use it as a Python package.
Installation is the same for both the command line interface tool and the Python package.
All of the commands on this page should be run at the command line. On macOS, this can be done in the terminal (⌘+space, "Terminal"). On Windows, this can be done in a command prompt, or if you installed Anaconda an anaconda prompt (Start > Anaconda3 > Anaconda Prompt).
How do I organize my videos for
You can specify the path to a directory of videos or specify a list of filepaths in a
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.
For example, say we have a directory of videos called
example_vids that we want to generate predictions for using
zamba. Let's list the videos:
$ ls example_vids/ blank.mp4 chimp.mp4 eleph.mp4 leopard.mp4
Here are some screenshots from those videos:
In this example, the videos have meaningful names so that we can easily
compare the predictions made by
zamba. In practice, your videos will
probably be named something much less useful!
To generate and save predictions for your videos using the default settings, run:
$ zamba predict --data-dir example_vids/
zamba will output a
.csv file with rows labeled by each video filename and columns for each class (ie. species). The default prediction will store all class probabilities, so that cell
(i,j) is the probability that animal
j is present in video
i. Comprehensive predictions are helpful when a single video contains multiple species.
Predictions will be saved to
zamba_predictions.csv in the current working directory by default. You can save out predictions to a different folder using the
Adding the argument
--output-class-names will simplify the predictions to return only the most likely animal in each video:
$ zamba predict --data-dir example_vids/ --output-class-names $ cat zamba_predictions.csv blank.mp4,blank chimp.mp4,chimpanzee_bonobo eleph.mp4,elephant leopard.mp4,leopard
There are three pretrained models that ship with
european. Which model you should use depends on your priorities and geography (see the Available Models page for more details). By default
zamba will use the
time_distributed model. Add the
--model argument to specify one of other options:
$ zamba predict --data-dir example_vids/ --model slowfast
Training a model¶
You can continue training one of the models that ships with
zamba by either:
- Finetuning with additional labeled videos where the species are included in the list of
- Finetuning with labeled videos that include new species
In either case, the commands for training are the same. Say that we have labels for the videos in the
example_vids folder saved in
example_labels.csv. To train a model, run:
$ zamba train --data-dir example_vids/ --labels example_labels.csv
The labels file must have columns for both filepath and label. The filepath column should contain either absolute paths or paths relative to the
data-dir. Optionally, there can also be columns for
site. Let's print the example labels:
$ cat example_labels.csv filepath,label blank.MP4,blank chimp.MP4,chimpanzee_bonobo eleph.MP4,elephant leopard.MP4,leopard
By default, the trained model and additional training output will be saved to a
version_n folder in the current working directory. For example,
$ zamba train --data-dir example_vids/ --labels example_labels.csv $ ls version_0/ hparams.yaml time_distributed.ckpt train_configuration.yamml val_metrics.json ...
Downloading model weights¶
zamba needs to download the "weights" files for the models it uses to make predictions. On first run, it will download ~200-500 MB of files with these weights depending which model you choose.
Once a model's weights are downloaded,
zamba will use the local version and will not need to perform this download again. If you are not in the United States, we recommend running the above command with the additional flag either
--weight_download_region eu or
--weight_download_region asia depending on your location. The closer you are to the server, the faster the downloads will be.
Once zamba is installed, you can see more details of each function with
--help. For example, you can run
zamba predict --help:
Usage: zamba predict [OPTIONS] Identify species in a video. This is a command line interface for prediction on camera trap footage. Given a path to camera trap footage, the predict function use a deep learning model to predict the presence or absense of a variety of species of common interest to wildlife researchers working with camera trap data. If an argument is specified in both the command line and in a yaml file, the command line input will take precedence. Options: --data-dir PATH Path to folder containing videos. --filepaths PATH Path to csv containing `filepath` column with videos. --model [time_distributed|slowfast|european] Model to use for inference. Model will be superseded by checkpoint if provided. [default: time_distributed] --checkpoint PATH Model checkpoint path to use for inference. If provided, model is not required. --gpus INTEGER Number of GPUs to use for inference. If not specifiied, will use all GPUs found on machine. --batch-size INTEGER Batch size to use for training. --save / --no-save Whether to save out predictions. If you want to specify the output directory, use save_dir instead. --save-dir PATH An optional directory in which to save the model predictions and configuration yaml. Defaults to the current working directory if save is True. --dry-run / --no-dry-run Runs one batch of inference to check for bugs. --config PATH Specify options using yaml configuration file instead of through command line options. --proba-threshold FLOAT Probability threshold for classification between 0 and 1. If specified binary predictions are returned with 1 being greater than the threshold, 0 being less than or equal to. If not specified, probabilities between 0 and 1 are returned. --output-class-names / --no-output-class-names If True, we just return a video and the name of the most likely class. If False, we return a probability or indicator (depending on --proba_threshold) for every possible class. --num-workers INTEGER Number of subprocesses to use for data loading. --weight-download-region [us|eu|asia] Server region for downloading weights. --skip-load-validation / --no-skip-load-validation Skip check that verifies all videos can be loaded prior to inference. Only use if you're very confident all your videos can be loaded. -o, --overwrite Overwrite outputs in the save directory if they exist. -y, --yes Skip confirmation of configuration and proceed right to prediction. --help Show this message and exit.
Or if you are training a model, you can run
zamba train --help:
$ zamba train --help Usage: zamba train [OPTIONS] Train a model on your labeled data. If an argument is specified in both the command line and in a yaml file, the command line input will take precedence. Options: --data-dir PATH Path to folder containing videos. --labels PATH Path to csv containing video labels. --model [time_distributed|slowfast|european] Model to train. Model will be superseded by checkpoint if provided. [default: time_distributed] --checkpoint PATH Model checkpoint path to use for training. If provided, model is not required. --config PATH Specify options using yaml configuration file instead of through command line options. --batch-size INTEGER Batch size to use for training. --gpus INTEGER Number of GPUs to use for training. If not specifiied, will use all GPUs found on machine. --dry-run / --no-dry-run Runs one batch of train and validation to check for bugs. --save-dir PATH An optional directory in which to save the model checkpoint and configuration file. If not specified, will save to a `version_n` folder in your working directory. --num-workers INTEGER Number of subprocesses to use for data loading. --weight-download-region [us|eu|asia] Server region for downloading weights. --skip-load-validation / --no-skip-load-validation Skip check that verifies all videos can be loaded prior to training. Only use if you're very confident all your videos can be loaded. -y, --yes Skip confirmation of configuration and proceed right to training. --help Show this message and exit.