Skip to content

Latest commit

 

History

History
49 lines (47 loc) · 2.86 KB

CHANGELOG.md

File metadata and controls

49 lines (47 loc) · 2.86 KB
Raw

Changelog

  • 0.2.22 (dev):

    • Bugfix: torch code was broken due to changes in torch 1.11
    • Bugfix: SALICON dataset download did not work anymore
    • Bugfix: NUSEF datast links changed

  • 0.2.21:

    • Added new datasets: PASCAL-S and DUT-OMRON
    • Feature: FixedStimulusSizeModel and DVAAwareModel
    • Feature: Fixations finally support len()
    • Experimental feature: conditional_log_densities(stimuli, fixations) and conditional_saliency_maps(...). This is WIP to enable batch processing in models.
    • Fallback models for stimulus dependent models
    • MixtureScanpathModel
    • Reimplemented AUC for special case of only one positive sample, leading to substantial speedup
    • There is a new version of the CAT2000 train dataset which fixes some details in the processing. Since it changes the dataset, by default the old processing is used.
    • Feature: ShuffledSimpleBaselineModel. Baseline model to be used with ShuffledAUCSaliencyMapModel in cases where using ShuffledBaselineModel is not feasible.
    • pysaliency.get_toronto now returns a Fixations instance instead of FixationTrains since we don not have scanpath information.
    • pysaliency.baseline_utils.KDEGoldModel now supports a keyword argument grid_spacing which controls how densly the log density of the KDEModel is computed before it is linearly interpolated. This can substantially speed up computations on high resolution images.
    • Feature: pysaliency.precomputed_models.SaliencyMapModelFromArchive and ModelFromArchive for loading model predictions from ZIP, TAR and RAR files.
    • Bugfix: all matlab scripts where missing in the pip installation since the change to setuptools.

  • 0.2.20:

    • Stimuli now support attributes, just like Fixations. The CAT2000 train and test datasets now have the stimulus categories as attribute.
    • failure to download and setup a dataset will no longer result in leftover dataset files that keep pysaliency from trying again.
    • crossvalidation splits now support stratifying stimulus attributes
    • the MIT1003 dataset now also contains the history of fixation durations
    • FixationIndexDependentModel
    • Bugfix: The CC of a constant saliency map wrt to a nonconstant one now returns zero (instead of nan as previously).
    • Feature: Added keyword argument attributes to Fixations constructor
    • Feature: Provide KLDiv and SIM as functions that can be applied to saliency maps without need for a model.

  • 0.2.19:

    • added pytorch implementation for optimization of similarity metric as alternative to tensorflow implementation which still uses tensorflow 1.x
    • added pytorch implementation for saliency map processing as alternative to theano implementation.
    • removed obsolete dependency on openmp
    • made import of pytorch, theano and tensorflow optional
    • bugfixes in precomputed models for stimuli sets with nested directories