zamba Command Line Interface¶
This section goes into a bit more detail concerning the available options for
zamba command line interface tool. If you are new to
zamba and just
want to classify some videos as soon as possible, see the [Quickstart]
zamba’s Optional Flags¶
As discussed in the Quickstart, the
--help flag provides
more information about options for
$ zamba predict --help Usage: zamba predict [OPTIONS] [DATA_PATH] [PRED_PATH] 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. Options: --tempdir PATH Path to temporary directory. If not specified, OS temporary directory is used. --proba_threshold FLOAT Probability threshold for classification. 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 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. --model_profile TEXT Defaults to 'full' which is slow and accurate; can be 'fast' which is faster and less accurate. --weight_download_region TEXT Defaults to 'us', can also be 'eu' or 'asia'. Region for server to download weights. --verbose Displays additional logging information during processing. --help Show this message and exit.
Let’s go through these one by one.
This option specifies the
PATH to be used for temporary storage during
prediction. The model that is shipped with
zamba is able to process within
memory (assuming reasonably large modern memory of ~16 GB). If a custom model
is being used, it may be necessary to point
zamba to a mounted drive or some
other large-capacity directory. By default this uses the operating system’s temporary directory.
For advanced uses, you may want the algorithm to be more or less sensitive to if a species is present. This parameter is a
FLOAT number, e.g.,
0.64 corresponding to the probability
threshold beyond which an animal is considered to be present in the video being
By default no threshold is passed,
proba_threshold=None. This will return a probability from 0-1 for each species that could occur in each video. If a threshold is passed,
then the final prediction value returned for each class is
probability >= proba_threshold, so that all class values become
False, the species does not appear) or
True, the species does appear).
Setting this option to
True yields the most concise output
zamba is capable
of. The highest species probability in a video is taken to be the only
species in that video, and the output returned is simply the video name and
the name of the species (or
blank) with the highest class probability. See
the Quickstart for example usage.
There are two versions of the algorithm that ship with zamba. If you pass
fast there is a faster algorithm that can be less accurate that is used. If you pass
full (the default) a slower algorithm that has 4 sub-models instead of 1 is used.
zamba needs to download pretrained weights for the neural network architecture, we make these weights available in different regions. ‘us’ is the default, but if you are not in the US you should use either
eu for the European Union or
asia for Asia Pacific to make sure that these download as quickly as possible for you.
This option currently controls only whether or not the command line shows additional information during processing.