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https://github.com/ZFTurbo/Weighted-Boxes-Fusion
Set of methods to ensemble boxes from different object detection models, including implementation of "Weighted boxes fusion (WBF)" method.
https://github.com/ZFTurbo/Weighted-Boxes-Fusion
boxes ensemble-prediction object-detection
Last synced: about 1 month ago
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Set of methods to ensemble boxes from different object detection models, including implementation of "Weighted boxes fusion (WBF)" method.
- Host: GitHub
- URL: https://github.com/ZFTurbo/Weighted-Boxes-Fusion
- Owner: ZFTurbo
- License: mit
- Created: 2019-10-27T16:17:58.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-01-10T17:36:59.000Z (over 1 year ago)
- Last Synced: 2024-07-05T14:03:29.619Z (2 months ago)
- Topics: boxes, ensemble-prediction, object-detection
- Language: Python
- Homepage:
- Size: 68.4 KB
- Stars: 1,693
- Watchers: 17
- Forks: 235
- Open Issues: 35
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.md
- License: LICENSE
Awesome Lists containing this project
README
[](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, Numba
# 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](examples/example.py)
#### 3D version
There is support for 3D boxes in WBF method with `weighted_boxes_fusion_3d` function. Check example of usage in [example_3d.py](examples/example_3d.py)
#### 1D version
There is support for 1D line segments in WBF method with `weighted_boxes_fusion_1d` function. Check example of usage in [example_1d.py](examples/example_1d.py). It was reported that 1D variant can be useful in Named-entity recognition (NER) type of tasks for Natural Language Processing (NLP) problems. Check discussion [here](https://www.kaggle.com/c/feedback-prize-2021/discussion/313389).
## Benchmarks
* Benchmark for [Open Images Dataset (5 models)](benchmark_oid/README.md)
* Benchmark for [COCO Dataset (10 models)](benchmark_coco/README.md)
* Benchmark for [NLP Dataset (10 models)](benchmark_nlp/README.md) - example for one-dimensional WBF variant
## Description of WBF method and citation
* https://arxiv.org/abs/1910.13302 (updated: 2020.08)
* https://authors.elsevier.com/c/1ca0dxnVK3cWY
If you find this code useful please cite:
```
@article{solovyev2021weighted,
title={Weighted boxes fusion: Ensembling boxes from different object detection models},
author={Solovyev, Roman and Wang, Weimin and Gabruseva, Tatiana},
journal={Image and Vision Computing},
pages={1-6},
year={2021},
publisher={Elsevier}
}
```