GitLab

Unfreeze keras version as incorrect version issue was fixed. This also resolved the endless TF ODT API pip install issue.

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Instead of searching the annotation files based on class names in the filenames, read all the XML annotation files. For the cats/dogs use-case, just delete all the XML files that are not the breeds we care about.

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such that NMS won't wash out the rescored instance scores.

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Also upgrades keras version due to
https://github.com/tensorflow/tensorflow/issues/51592

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the image/object/keypoint/text feature but not in the keypoint label map.

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- Added configurable arguments in CenterNet for maximum normalized coordinate (this might be useful for handling cases where the bounding box can only be presented out-of-bound with the direction-of-path point at the center in panorama rotation).

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datasets which contain different subset of keypoints.

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that maps from the keypoint names to their ids.

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using 5d tensors to avoid issue when converted to TfLite model.

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CenterNet meta arch to avoid using scatter_nd and tf.cond.

Also, use static_or_dynamic_map_fn in the convert_strided_predictions_to_normalized_keypoints function to avoid introducing cycles in the graph that causes the TPU converter errors.

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runs much slower in the browser.

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The order of the input tensors is incorrect and therefore the sample notebook does not run.

The sample code has the order of the input tensors as `boxes, classes, scores, num_detections`, but actually the order should be `scores, boxes,  num_detections, classes`

After correcting the indexes of the input tensor, the sample notebook runs.

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  * Replaced the deprecated `to_int/to_float` in `center_net_meta_arch.py` with `tf.cast()` (following the warning).
  * Added `max_norm_coord` argument to CenterNet source code to allow controlling the maximum coordinate value to be considered, to allow for the wrap-around special case for RxR data.

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true_image_shape is not required and can be inferred by the prediction tensors.

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