bee_analysis

Analyze some bee videos. Could also work on other data too.

Dependencies

Torch, torchvision, ffmpeg (or possibly ffmpeg-python), and webdataset.

In general, install through pip3 or conda:

pip3 install torch torchvission webdataset ffmpeg

See https://pytorch.org/get-started/locally/ for additional installation instructions

Creating a dataset

First run make_train_csv.sh to create a csv file with labels for each video.

bash make_train_csv.sh path/to/videos > dataset.csv

The file paths created by make_train_csv.sh are relative so it should be run from the same directory as the dataprep step will be run.

Next process that csv file with VidActRecDataprep.py. For example:

python3 VidActRecDataprep.py --width 400 --height 400 --resize-strategy crop --samples 500 --crop_noise 20 --out_channels 1 --frames_per_sample 1 dataset.csv dataset.tar

The crop_nose option adds some randomness to the cropping location, which is important to prevenet overfitting.

The script can also be run with --help for more details.

Training a model

Train a model with the VidActRecTrain.py script. For example:

python3 VidActRecTrain.py --epochs 10 --modeltype alexnet --evaluate eval.tar train.tar

By default the model is saved to model.checkpoint, but that can be changed with the --outname option. Run the script with --help for more details.