- 27 Feb, 2018 1 commit
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Zhichao Lu authored
187187978 by Zhichao Lu: Only updating hyperparameters if they have non-null values. -- 187097690 by Zhichao Lu: Rewrite some conditions a bit more clearly. -- 187085190 by Zhichao Lu: More informative error message. -- 186935376 by Zhichao Lu: Added option to evaluator.evaluate to use custom evaluator objects. -- 186808249 by Zhichao Lu: Fix documentation re: number of stages. -- 186775014 by Zhichao Lu: Change anchor generator interface to return a list of BoxLists containing anchors for different feature map layers. -- 186729028 by Zhichao Lu: Minor fixes to object detection. -- 186723716 by Zhichao Lu: Fix tf_example_decoder.py initailization issue. -- 186668505 by Zhichao Lu: Remove unused import. -- 186475361 by Zhichao Lu: Update the box predictor interface to return list of predictions - one from each feature map - instead of stacking them into one large tensor. -- 186410844 by Zhichao Lu: Fix PythonPath Dependencies. -- 186365384 by Zhichao Lu: Made some of the functions in exporter public so they can be reused. -- 186341438 by Zhichao Lu: Re-introducing check that label-map-path must be a valid (non-empty) string prior to overwriting pipeline config. -- 186036984 by Zhichao Lu: Adding default hyperparameters and allowing for overriding them via flags. -- 186026006 by Zhichao Lu: Strip `eval_` prefix from name argument give to TPUEstimator.evaluate since it adds the same prefix internally. -- 186016042 by Zhichao Lu: Add an option to evaluate models on training data. -- 185944986 by Zhichao Lu: let _update_label_map_path go through even if the path is empty -- 185860781 by Zhichao Lu: Add random normal initializer option to hyperparams builder. Scale the regression losses outside of the box encoder by adjusting huber loss delta and regression loss weight. -- 185846325 by Zhichao Lu: Add an option to normalize localization loss by the code size(number of box coordinates) in SSD Meta architecture. -- 185761217 by Zhichao Lu: Change multiscale_grid_anchor_generator to return anchors in normalized coordinates by default and add option to configure it. In SSD meta architecture, TargetAssigner operates in normalized coordinate space (i.e, groundtruth boxes are in normalized coordinates) hence we need the option to generate anchors in normalized coordinates. -- 185747733 by Zhichao Lu: Change the smooth L1 localization implementationt to use tf.losses.huber_loss and expose the delta parameter in the proto. -- 185715309 by Zhichao Lu: Obviates the need for prepadding on mobilenet v1 and v2 for fully convolutional models. -- 185685695 by Zhichao Lu: Fix manual stepping schedule to return first rate when there are no boundaries -- 185621650 by Zhichao Lu: Added target assigner proto for configuring negative class weights. -- PiperOrigin-RevId: 187187978
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- 01 Feb, 2018 1 commit
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Zhichao Lu authored
184048729 by Zhichao Lu: Modify target_assigner so that it creates regression targets taking keypoints into account. -- 184027183 by Zhichao Lu: Resnet V1 FPN based feature extractors for SSD meta architecture in Object Detection V2 API. -- 184004730 by Zhichao Lu: Expose a lever to override the configured mask_type. -- 183933113 by Zhichao Lu: Weight shared convolutional box predictor as described in https://arxiv.org/abs/1708.02002 -- 183929669 by Zhichao Lu: Expanding box list operations for future data augmentations. -- 183916792 by Zhichao Lu: Fix unrecognized assertion function in tests. -- 183906851 by Zhichao Lu: - Change ssd meta architecture to use regression weights to compute loss normalizer. -- 183871003 by Zhichao Lu: Fix config_util_test wrong dependency. -- 183782120 by Zhichao Lu: Add __init__ file to third_party directories. -- 183779109 by Zhichao Lu: Setup regular version s...
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- 27 Oct, 2017 1 commit
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Vivek Rathod authored
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- 21 Sep, 2017 1 commit
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Neal Wu authored
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- 15 Jun, 2017 1 commit
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derekjchow authored
For details see our paper: "Speed/accuracy trade-offs for modern convolutional object detectors." Huang J, Rathod V, Sun C, Zhu M, Korattikara A, Fathi A, Fischer I, Wojna Z, Song Y, Guadarrama S, Murphy K, CVPR 2017 https://arxiv.org/abs/1611.10012
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