2020年全国水下机器人（湛江）大赛源码+学习说明.zip

[](https://zenodo.org/badge/latestdoi/217881799) ## Weighted boxes fusion Repository contains Python implementation of several methods for ensembling boxes from object detection models: * Non-maximum Suppression (NMS) * Soft-NMS [[1]](https://arxiv.org/abs/1704.04503) * Non-maximum weighted (NMW) [[2]](http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w14/Zhou_CAD_Scale_Invariant_ICCV_2017_paper.pdf) * **Weighted boxes fusion (WBF)** [[3]](https://arxiv.org/abs/1910.13302) - new method which gives better results comparing to others ## Requirements Python 3.*, Numpy # Installation `pip install ensemble-boxes` ## Usage examples Coordinates for boxes expected to be normalized e.g in range [0; 1]. Order: x1, y1, x2, y2. Example of boxes ensembling for 2 models below. * First model predicts 5 boxes, second model predicts 4 boxes. * Confidence scores for each box model 1: [0.9, 0.8, 0.2, 0.4, 0.7] * Confidence scores for each box model 2: [0.5, 0.8, 0.7, 0.3] * Labels (classes) for each box model 1: [0, 1, 0, 1, 1] * Labels (classes) for each box model 2: [1, 1, 1, 0] * We set weight for 1st model to be 2, and weight for second model to be 1. * We set intersection over union for boxes to be match: iou_thr = 0.5 * We skip boxes with confidence lower than skip_box_thr = 0.0001 ```python from ensemble_boxes import * boxes_list = [[ [0.00, 0.51, 0.81, 0.91], [0.10, 0.31, 0.71, 0.61], [0.01, 0.32, 0.83, 0.93], [0.02, 0.53, 0.11, 0.94], [0.03, 0.24, 0.12, 0.35], ],[ [0.04, 0.56, 0.84, 0.92], [0.12, 0.33, 0.72, 0.64], [0.38, 0.66, 0.79, 0.95], [0.08, 0.49, 0.21, 0.89], ]] scores_list = [[0.9, 0.8, 0.2, 0.4, 0.7], [0.5, 0.8, 0.7, 0.3]] labels_list = [[0, 1, 0, 1, 1], [1, 1, 1, 0]] weights = [2, 1] iou_thr = 0.5 skip_box_thr = 0.0001 sigma = 0.1 boxes, scores, labels = nms(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr) boxes, scores, labels = soft_nms(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr, sigma=sigma, thresh=skip_box_thr) boxes, scores, labels = non_maximum_weighted(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr, skip_box_thr=skip_box_thr) boxes, scores, labels = weighted_boxes_fusion(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr, skip_box_thr=skip_box_thr) ``` #### Single model If you need to apply NMS or any other method to single model predictions you can call function like that: ```python from ensemble_boxes import * # Merge boxes for single model predictions boxes, scores, labels = weighted_boxes_fusion([boxes_list], [scores_list], [labels_list], weights=None, method=method, iou_thr=iou_thr, thresh=thresh) ``` More examples can be found in [example.py](./example.py) ## Accuracy and speed comparison Comparison was made for ensemble of 5 different object detection models predictions trained on [Open Images Dataset](https://storage.googleapis.com/openimages/web/index.html) (500 classes). Model scores at local validation: * Model 1: mAP(0.5) 0.5164 * Model 2: mAP(0.5) 0.5019 * Model 3: mAP(0.5) 0.5144 * Model 4: mAP(0.5) 0.5152 * Model 5: mAP(0.5) 0.4910 | Method | mAP(0.5) Result | Best params | Elapsed time (sec) | | ------ | --------------- | ----------- | ------------ | | NMS | **0.5642** | IOU Thr: 0.5 | 47 | | Soft-NMS | **0.5616** | Sigma: 0.1, Confidence Thr: 0.001 | 88 | | NMW | **0.5667** | IOU Thr: 0.5 | 171 | | WBF | **0.5982** | IOU Thr: 0.6 | 249 | You can download model predictions as well as ground truth labels from here: [test_data.zip](https://github.com/ZFTurbo/Weighted-Boxes-Fusion/releases/download/v1.0/test_data.zip) Ensemble script for them is available here: [example_oid.py](./example_oid.py) ## Description of WBF method * https://arxiv.org/abs/1910.13302