Computes and stores the average and current value

CNN Feature Map Embedding Extract feature map from CNN, flatten, project to embedding dim.

r"""Implement of large margin arc distance: : Args: in_features: size of each input sample out_features: size of each

Center loss. Reference: Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016. Args: num_cl

r"""Implement of large margin cosine distance: : Args: in_features: size of each input sample out_features: size of each output sa

Image to Patch Embedding

Randomly sample N identities, then for each identity, randomly sample K instances, therefore batch size is N*K. Args: - data_source (

Vision Transformer with support for patch or hybrid CNN input stage

Vision Transformer with support for patch or hybrid CNN input stage

AugMix Transform Adapted and improved from impl here: https://github.com/google-research/augmix/blob/master/imagenet.py From paper: 'AugMix:

Cosine decay with restarts. This is described in the paper https://arxiv.org/abs/1608.03983. Inspiration from https://github.com/alle

Cross entropy loss with label smoothing regularizer. Reference: Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVP

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

CNN Feature Map Embedding Extract feature map from CNN, flatten, project to embedding dim.

Image to Patch Embedding

Image to Patch Embedding

Randomly selects a rectangle region in an image and erases its pixels. 'Random Erasing Data Augmentation' by Zhong et al. See https:/

Randomly sample N identities, then for each identity, randomly sample K instances, therefore batch size is N*K. Args: - data_source (

Base class of reid dataset

Base class of image reid dataset

Image to Patch Embedding

Office Home

Office

Office Home

Image to Patch Embedding

Parameter Scheduler Base Class A scheduler base class that can be used to schedule any optimizer parameter groups. Unlike the builtin PyTorch

Triplet loss using HARDER example mining, modified based on original triplet loss using hard example mining

Office Home

Vision Transformer with support for patch or hybrid CNN input stage

Vision Transformer with support for patch or hybrid CNN input stage