GitLab

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to export CenterNet model by passing in flags  without changing the pipeline configuration.

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MobileNetV2Fpn feature extractor.

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keypoint depth.

Updated model_lib files to populate the keypoint depth information.

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properly changed if the keep_aspect_ratio_resizer was used in conjunction with
pad_to_max_dimension.

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depth and weight fields.

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Updated the tf_example decoder and decoder builders to parse the keypoint depth features from the input tf.Example.

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estimation. It is designed specifically to run the model in the browser
environment using tf.js.

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FPN network.

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for object detection and keypoint estimation tasks.

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by default NMS is off.

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Context R-CNN Updates: Added capabilities to use multi-headed or multi-layered attention, to place the attention heads pre- or post-second stage feature extraction, and to work with embedded features in the context feature back from pre- or post-second stage feature extraction. Added an option for RPN feature map crops to be piped through to model outputs.

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DEFAULT_VALUE_OK=setting prefetch batches default to 2 as using -1 can cause memory issues for some models such as context r-cnn that use sequence examples.

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Updating Context R-CNN dataset tools to fix a bug in context feature bank building logic when the number of context examples in the time horizon is greater than the specified maximum, and adding capabilities to track and save the number of embeddings stored per image.

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added a new operating mode for single-instance prediction.

1) Refactored the _postprocess_keypoints_for_class_and_image function such that
it can be reused by single/multi class keypoint tasks.
2) Removed the "mod" operator to make the model compatible with WASM.

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Avoid  explicit tf.unstack in `convert_strided_predictions_to_normalized_boxes`. This enables exporting a saved model with dynamic batch size.

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