wehub-resource-sync
39 lines
3.3 KiB
Python
39 lines
3.3 KiB
Python
FLOATING_POINT_COMPARISON = 1e-6 # floating point comparison for fuzzy equals
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CLIPPING_LOWER_BOUND = 1e-6 # lower-bound clipping threshold for expected behavior
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CONFIDENT_THRESHOLDS_LOWER_BOUND = (
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2 * FLOATING_POINT_COMPARISON
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) # lower bound imposed to clip confident thresholds from below, has to be larger than floating point comparison
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TINY_VALUE = 1e-100 # very tiny value for clipping
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# Object Detection Constants
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EUC_FACTOR = 0.1 # Factor to control magnitude of euclidian distance. Increasing the factor makes the distances between two objects go to zero more rapidly.
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MAX_ALLOWED_BOX_PRUNE = 0.97 # This is max allowed percent of boxes that are pruned before a warning is thrown given a specific threshold. Pruning too many boxes negatively affects performance.
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IOU_THRESHOLD = (
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0.5 # Threshold for considering the predicted box and annotated box to be overlapping
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)
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EPSILON = 1e-6 # Small value to prevent division by zero
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ALPHA = 0.9 # Param for objectlab, weight between IoU and distance when considering similarity matrix. High alpha means considering IoU more strongly over distance
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LOW_PROBABILITY_THRESHOLD = 0.5 # Param for get_label_quality_score, lowest predicted class probability threshold allowed when considering predicted boxes to identify badly located label boxes.
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HIGH_PROBABILITY_THRESHOLD = 0.95 # Param for objectlab, high probability threshold for considering predicted boxes to identify overlooked and swapped label boxes
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TEMPERATURE = 0.1 # Param for objectlab, temperature of the softmin function used to pool the per-box quality scores for an error subtype across all boxes into a single subtype score for the image. With a lower temperature, softmin pooling acts more like minimum pooling, alternatively it acts more like mean pooling with high temperature.
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LABEL_OVERLAP_THRESHOLD = 0.95 # Param for objectlab, minimum IoU threshold for deciding when two boxes overlap used for deciding which objects have multiple conflicting annotations.
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OVERLOOKED_THRESHOLD_FACTOR = 0.8 # Param for find_label_issues. Per-box label quality score threshold scale factor to determine max score for a box to be considered an overlooked issue
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BADLOC_THRESHOLD_FACTOR = 0.8 # Param for find_label_issues. Per-box label quality score threshold scale factor to determine max score for a box to be considered a bad location issue
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SWAP_THRESHOLD_FACTOR = 0.8 # Param for find_label_issues. Per-box label quality score threshold scale factor to determine max score for a box to be considered a swap issue
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AP_SCALE_FACTOR = 0.25 # Param for find_label_issues. Scale factor for per-class precision to determine is_issue.
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CUSTOM_SCORE_WEIGHT_OVERLOOKED = (
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1 / 3
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) # Param for get_label_quality_score, weight to determine how much to value overlooked scores over other subtypes when deciding the overall label quality score for an image.
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CUSTOM_SCORE_WEIGHT_BADLOC = (
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1 / 3
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) # Param for get_label_quality_score, weight to determine how much to value badloc scores over other subtypes when deciding issues
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CUSTOM_SCORE_WEIGHT_SWAP = (
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1 / 3
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) # Param for get_label_quality_score, weight to determine how much to value swap scores over other subtypes when deciding issues
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MAX_CLASS_TO_SHOW = 10 # Number of classes to show in legend during the visualize method. Classes over max_class_to_show are cut off.
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