v1.2

• Added example script for descriptor extraction with different publicly available models
• Added the MIT license
• Added mutli-scale performance on roxford5k and rparis6k for new pre-trained networks with end-to-end whitening, trained on both retrieval-SfM-120 and google-landmarks-2018 train datasets
• Added a new example test script without post-processing, for networks that are trained in a fully end-to-end manner, with whitening as FC layer learned during training
• Added few things in train example: GeMmp pooling, triplet loss, small trick to handle really large batches
• Added more pre-computed whitening options in imageretrievalnet
• Added triplet loss
• Added GeM pooling with multiple parameters (one p per channel/dimensionality)
• Added script to enable download on Windows 10 as explained in Issue #39, courtesy of SongZRui
• Fixed cropping of down-sampled query image

v1.1

• Migrated code to PyTorch 1.0.0, removed Variable, added torch.no_grad for more speed and less memory at evaluation
• Added rigid grid regional pooling that can be combined with any global pooling method (R-MAC, R-SPoC, R-GeM)
• Added PowerLaw normalization layer
• Added multi-scale testing with any given set of scales, in example test script
• Fix related to precision errors of covariance matrix estimation during whitening learning
• Fixed minor bugs

v1.0

• First public version
• Compatible with PyTorch 0.3.0