GitLab

196161788  by Zhichao Lu:

    Add eval_on_train_steps parameter.

    Since the number of samples in train dataset is usually different to the number of samples in the eval dataset.

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196151742  by Zhichao Lu:

    Add an optional random sampling process for SSD meta arch and update mean stddev coder to use default std dev when corresponding tensor is not added to boxlist field.

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196148940  by Zhichao Lu:

    Release ssdlite mobilenet v2 coco trained model.

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196058528  by Zhichao Lu:

    Apply FPN feature map generation before we add additional layers on top of resnet feature extractor.

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195818367  by Zhichao Lu:

    Add support for exporting detection keypoints.

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195745420  by Zhichao Lu:

    Introduce include_metrics_per_category option to Object Detection eval_config.

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195734733  by Zhichao Lu:

    Rename SSDLite config to be more explicit.

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195717383  by Zhichao Lu:

    Add quantized training to object_detection.

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195683542  by Zhichao Lu:

    Fix documentation for the interaction of fine_tune_checkpoint_type and load_all_detection_checkpoint_vars interaction.

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195668233  by Zhichao Lu:

    Using batch size from params dictionary if present.

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195570173  by Zhichao Lu:

    A few fixes to get new estimator API eval to match legacy detection eval binary by (1) plumbing `is_crowd` annotations through to COCO evaluator, (2) setting the `sloppy` flag in tf.contrib.data.parallel_interleave based on whether shuffling is enabled, and (3) saving the original image instead of the resized original image, which allows for small/medium/large mAP metrics to be properly computed.

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195316756  by Zhichao Lu:

    Internal change

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PiperOrigin-RevId: 196161788

187187978  by Zhichao Lu:

    Only updating hyperparameters if they have non-null values.

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187097690  by Zhichao Lu:

    Rewrite some conditions a bit more clearly.

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187085190  by Zhichao Lu:

    More informative error message.

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186935376  by Zhichao Lu:

    Added option to evaluator.evaluate to use custom evaluator objects.

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186808249  by Zhichao Lu:

    Fix documentation re: number of stages.

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186775014  by Zhichao Lu:

    Change anchor generator interface to return a list of BoxLists containing anchors for different feature map layers.

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186729028  by Zhichao Lu:

    Minor fixes to object detection.

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186723716  by Zhichao Lu:

    Fix tf_example_decoder.py initailization issue.

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186668505  by Zhichao Lu:

    Remove unused import.

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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.

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186410844  by Zhichao Lu:

    Fix PythonPath Dependencies.

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186365384  by Zhichao Lu:

    Made some of the functions in exporter public so they can be reused.

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186341438  by Zhichao Lu:

    Re-introducing check that label-map-path must be a valid (non-empty) string prior to overwriting pipeline config.

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186036984  by Zhichao Lu:

    Adding default hyperparameters and allowing for overriding them via flags.

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186026006  by Zhichao Lu:

    Strip `eval_` prefix from name argument give to TPUEstimator.evaluate since it adds the same prefix internally.

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186016042  by Zhichao Lu:

    Add an option to evaluate models on training data.

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185944986  by Zhichao Lu:

    let _update_label_map_path go through even if the path is empty

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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.

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185846325  by Zhichao Lu:

    Add an option to normalize localization loss by the code size(number of box coordinates) in SSD Meta architecture.

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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.

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185747733  by Zhichao Lu:

    Change the smooth L1 localization implementationt to use tf.losses.huber_loss and expose the delta parameter in the proto.

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185715309  by Zhichao Lu:

    Obviates the need for prepadding on mobilenet v1 and v2 for fully convolutional models.

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185685695  by Zhichao Lu:

    Fix manual stepping schedule to return first rate when there are no boundaries

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185621650  by Zhichao Lu:

    Added target assigner proto for configuring negative class weights.

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PiperOrigin-RevId: 187187978

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