SpeciesNet 4.0.1 Docker images available

Applied Machine Learning Group, University of Waikato

2025-03-05 13:21

First Docker images are available for the SpeciesNet network that Google announced on March 3rd, 2025:

Below is an example on how to use these images (on Linux or on Windows under WSL2).

Prerequisites:

create a directory for your output eg "speciesnet"
in that directory create the following sub-directories
- cache
- config
- data
- output

Processing data:

copy the images that you want to analyze into the "speciesnet/data" directory

from the "speciesnet" directory launch the appropriate docker image in interactive mode

CPU:

docker run --rm --gpus=all --shm-size 8G --net=host \
  -u $(id -u):$(id -g) -e USER=$USER \
  -v `pwd`:/workspace \
  -v `pwd`/cache:/.cache \
  -v `pwd`/config:/.config \
  -v `pwd`/cache:/.torch \
  -it waikatodatamining/speciesnet:4.0.1_cpu

CUDA:

docker run --rm --gpus=all --shm-size 8G --net=host \
  -u $(id -u):$(id -g) -e USER=$USER \
  -v `pwd`:/workspace \
  -v `pwd`/cache:/.cache \
  -v `pwd`/config:/.config \
  -v `pwd`/cache:/.torch \
  -it waikatodatamining/speciesnet:4.0.1_cuda21.1

run the following script to process your images:

speciesnet_run_model \
    --folders "/workspace/data" \
    --predictions_json "/workspace/output/predictions.json"

Or, if you want to run the individual steps separately:

speciesnet_run_model --detector_only \
    --folders "/workspace/data" \
    --predictions_json "/workspace/output/detections.json"
speciesnet_run_model --classifier_only \
    --folders "/workspace/data" \
    --detections_json "/workspace/output/detections.json" \
    --predictions_json "/workspace/output/classifications.json"
speciesnet_run_model --ensemble_only \
    --folders "/workspace/data" \
    --detections_json "/workspace/output/detections.json" \
    --classifications_json "/workspace/output/classifications.json" \
    --predictions_json "/workspace/output/predictions.json"

On your host system, the "speciesnet/output" directory will then contain the generated .json file(s), with "predictions.json" containing all the relevant information (classification and bbox).

For more information on the json output format:

https://github.com/google/cameratrapai/tree/main?tab=readme-ov-file#output-format