Wildlife Embeddings

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

Data Format

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