Review #6
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sahnilab
commented
May 12, 2023
model.py
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| # indices = tf.image.non_max_suppression( | ||
| # normalized_boxes, scores, self.proposal_count, | ||
| # self.nms_threshold, name="rpn_non_max_suppression") | ||
| indices = tf.image.non_max_suppression( |
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This file contains original SeBRe model logic, and results in invalid detection results, even when running detection using weights that were also trained with normal non_max_suppression.
| "POOL_SIZE 7\n", | ||
| "POST_NMS_ROIS_INFERENCE 1000\n", | ||
| "POST_NMS_ROIS_TRAINING 2000\n", | ||
| "ROI_POSITIVE_RATIO 0.33\n", | ||
| "RPN_ANCHOR_RATIOS [0.5, 1, 2]\n", | ||
| "RPN_ANCHOR_SCALES (32, 64, 128, 256, 512)\n", | ||
| "RPN_ANCHOR_SCALES (16, 32, 64, 128, 256)\n", |
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I played with a few different factors of these values, but they did not seem to improve the results.
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| # Non-max suppression | ||
| def nms(normalized_boxes, scores): | ||
| indices, scores = tf.image.non_max_suppression_with_scores( |
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This matches the call I made when generating the initial "softmax" weights, though running detection with the corresponding weights does not yield any results.
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