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cosanlab/py-feat: 0.4.0

Authors: Jin Hyun Cheong; Eshin Jolly; Luke Chang; Tiankang Xie; skbyrne; Matthew Kenney; Nathaniel Haines; +1 Authors

cosanlab/py-feat: 0.4.0

Abstract

Notes This is a large overhaul and refactor of some of the core testing and API functionality to make future development, maintenance, and testing easier. Notable highlights include: tighter integration with torch data loaders dropping opencv as a dependency experimental support for macOS m1 GPUs passing keyword arguments to underlying torch models for more control Detector Changes New you can now pass keyword arguments directly to the underlying pytorch/sklearn models on Detector initialization using dictionaries. For example you can do: detector = Detector(facepose_model_kwargs={'keep_top_k': 500}) to initialize img2pose to only use 500 instead of 750 features all .detect_* methods can also pass keyword arguments to the underlying pytorch/sklearn models, albeit these will be passed to their underlying __call__ methods SVM AU model has been retrained with new HOG feature PCA pipeline new XGBoost AU model with new HOG feature PCA pipeline .detect_image and .detect_video now display a tqdm progressbar new skip_failed_detections keyword argument to still generate a Fex object when processing multiple images and one or more detections fail Breaking the new default model for landmark detection was changed from mobilenet to mobilefacenet. the new default model for AU detection was changed to our new xgb model which gives continuous valued predictions between 0-1 remove support for fer emotion model remove support for jaanet AU model remove support for pnp facepose detector drop support for reading and manipulating Affectiva and FACET data .detect_image will no longer resize images on load as the new default for output_size=None. If you want to process images with batch_size > 1 and images differ in size, then you will be required to manually set output_size otherwise py-feat will raise a helpful error message Fex Changes New new .update_sessions() method that returns a copy of a Fex frame with the .sessions attribute updated, making it easy to chain operations .predict() and .regress() now support passing attributes to X and or Y using string names that match the attribute names: 'emotions' use all emotion columns (i.e. fex.emotions) 'aus' use all AU columns (i.e. fex.aus) 'poses' use all pose columns (i.e. fex.poses) 'landmarks' use all landmark columns (i.e. fex.landmarks) 'faceboxes' use all facebox columns (i.e. fex.faceboxes) You can also combine feature groups using a comma-separated string e.g. fex.regress(X='emotions,poses', y='landmarks') .extract_* methods now include std and sem. These are also included in .extract_summary() Breaking All Fex attributes have been pluralized as indicated below. For the time-being old attribute access will continue to work but will show a warning. We plan to formally drop support in a few versions .landmark -> .landmarks .facepose -> .poses .input -> .inputs .landmark_x -> .landmarks_x .landmark_y -> .landmarks_y .facebox -> .faceboxes Development changes test_pretrained_models.py is now more organized using pytest classes added tests for img2pose models added more robust testing for the interaction between batch_size and output_size General Fixes data loading with multiple images of potentially different sizes should be faster and more reliable fix bug in resmasknet that would give poor predictions when multiple faces were present and particularly small #150 #149 #148 #147 #145 #137 #134 #132 #131 #130 #129 #127 #121 #104

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average