A pytorch lightning implementation of the WBIA Piev2 Plugin: https://github.com/WildMeOrg/wbia-plugin-pie-v2.

A csv file should be created for your dataset with the following columns:

• annot: unique integer identifier for each datapoint
• image: image name (not full path)
• name: individual name (incomparable sides of the same individual should have different names)
• encounter: annotations with the same encounter will not be compared during validation or testing. If unsure set equal to the annot column.
• x: left bounding box coordinate
• y: top bounding box coordinate
• w: bounding box width
• h: bounding box height
• theta: rotation of the bounding box

During training we evaluate the model on the validation data every two epochs and retain the checkpoint with the highest 1-vs-all top1 accuracy.

• --name: name for logging

• --version: version number for logging

• --data-file: path to the data csv file

• --data-dir: path to the image directory

• --eval-cutoff: training is done with individuals with > eval-cutoff encounters, the rest are used for validation/testing

• --image-size: input image size

• --train-transforms: data augmentation for training, see src/data/transforms.py

• --eval-transforms: data augmentation for validation/testing

• --num-copies: see src/data/sampler.py

• --num-instances: see src/data/sampler.py

• --batch-size: batch size per gpu

• --num-workers: number of dataloader workers

• --embedding-dim: output embedding size

• --lr: learning rate

• --wd: weight decay

• --fixbase-epoch: freeze the weights of the model excluding fully-connected layers for this many epochs during training

• --margin: triplet loss margin

• --weight-t: triplet loss weight

• --weight-x: cross-entropy loss weight

• --gpus: number of gpus

• --max_epochs: maximum number of epochs

Also included are all the pytorch-lightning trainer flags: https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.trainer.trainer.Trainer.html#pytorch_lightning.trainer.trainer.Trainer