Comments (5)
Workaround: use conf_type='box_and_model_avg' instead of default conf_type='avg'
from weighted-boxes-fusion.
Looks fine. Thank you
from weighted-boxes-fusion.
Example:
from ensemble_boxes import * weigths = [0.2, 0.2, 0.2, 0.2, 0.2] pred_boxes = [] pred_scores = [] pred_labels = [] for _ in range(5): pred_boxes.append([[0. , 0. , 0.0001, 0.0001]]) pred_scores.append([1.]) pred_labels.append([0]) pred_boxes, pred_scores, pred_labels = weighted_boxes_fusion( pred_boxes, pred_scores, pred_labels, weights=weigths, iou_thr=0.4, skip_box_thr=0., allows_overflow=False ) print(pred_scores)
Actual result: score [0.2] Expected result: score [1]
Probably we need to change the line weighted_boxes[i][1] = weighted_boxes[i][1] * min(weights.sum(), len(clustered_boxes)) / weights.sum() -> weighted_boxes[i][1] = weighted_boxes[i][1] * min(len(weights), len(clustered_boxes)) / weights.sum()
Sorry, I didn't get the point of this line:
Probably we need to change the line
weighted_boxes[i][1] = weighted_boxes[i][1] * min(**weights.sum()**, len(clustered_boxes)) / weights.sum() -> weighted_boxes[i][1] = weighted_boxes[i][1] * min(**len(weights),** len(clustered_boxes)) / weights.sum()
In the above example, weights.sum()=1, len(clustered_boxes)) = 1, len(weights) = 5. So both two lines have the same output, did I misunderstand something here?
Thank you!
from weighted-boxes-fusion.
For line 1 variable is 1. For line 2 variable is 5. Why result must be the same?
from weighted-boxes-fusion.
For line 1 variable is 1. For line 2 variable is 5. Why result must be the same?
my bad, misreading the variable. Thanks for the clarification!
from weighted-boxes-fusion.
Related Issues (20)
- Incorrect conf_type='max' mode HOT 1
- Strange result if boxes are intersected inside one model HOT 2
- confidence score > 1 issue HOT 1
- Bbox is 'Nan' when weights have 0 HOT 4
- How to find the optimal hyper-parameters in the ensembling process HOT 6
- Can work with polygon object ? HOT 1
- Compatibility for Instance Segmentation HOT 1
- Building wheel for llvmlite ... error HOT 1
- How to use in Yolov5
- Implemetation on mobile phone HOT 4
- label list on SSD HOT 1
- run NMS on empty bounding box from one of ensembled models HOT 5
- Single instance NMS
- 2d bbox wbf compute result error
- Is WBF consider to be part of Ensemble Learning HOT 4
- Back propagation when multiple models are ensembled
- model fusion for rotating boxes
- Recall and Confusion matrix, also the confusion matrix, of the WBF HOT 5
- about map_boxes in run_benchmark_oid.py HOT 2
- AttributeError: module 'numpy' has no attribute 'str'
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from weighted-boxes-fusion.