https://github.com/dbolya/tide
A General Toolbox for Identifying Object Detection Errors
https://github.com/dbolya/tide
error-detection errors evaluation instance-segmentation object-detection toolbox
Last synced: 14 days ago
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A General Toolbox for Identifying Object Detection Errors
- Host: GitHub
- URL: https://github.com/dbolya/tide
- Owner: dbolya
- License: mit
- Created: 2020-07-16T19:14:39.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-03-31T20:18:30.000Z (about 2 years ago)
- Last Synced: 2025-04-01T09:27:08.429Z (21 days ago)
- Topics: error-detection, errors, evaluation, instance-segmentation, object-detection, toolbox
- Language: Python
- Homepage: https://dbolya.github.io/tide
- Size: 12 MB
- Stars: 714
- Watchers: 17
- Forks: 115
- Open Issues: 34
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# A General **T**oolbox for **I**dentifying Object **D**etection **E**rrors
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```
An easy-to-use, general toolbox to compute and evaluate the effect of object detection and instance segmentation on overall performance. This is the code for our paper: [TIDE: A General Toolbox for Identifying Object Detection Errors](https://dbolya.github.io/tide/paper.pdf) ([ArXiv](https://arxiv.org/abs/2008.08115)) [ECCV2020 Spotlight].
Check out our ECCV 2020 short video for an explanation of what TIDE can do:
[](https://youtu.be/McYFYU3PXcU)
# Installation
TIDE is available as a python package for python 3.6+ as [tidecv](https://pypi.org/project/tidecv/). To install, simply install it with pip:
```shell
pip3 install tidecv
```
The current version is v1.0.1 ([changelog](https://github.com/dbolya/tide/blob/master/CHANGELOG.md)).
# Usage
TIDE is meant as a drop-in replacement for the [COCO Evaluation toolkit](https://github.com/cocodataset/cocoapi), and getting started is easy:
```python
from tidecv import TIDE, datasets
tide = TIDE()
tide.evaluate(datasets.COCO(), datasets.COCOResult('path/to/your/results/file'), mode=TIDE.BOX) # Use TIDE.MASK for masks
tide.summarize() # Summarize the results as tables in the console
tide.plot() # Show a summary figure. Specify a folder and it'll output a png to that folder.
```
This prints evaluation summary tables to the console:
```
-- mask_rcnn_bbox --
bbox AP @ 50: 61.80
Main Errors
=============================================================
Type Cls Loc Both Dupe Bkg Miss
-------------------------------------------------------------
dAP 3.40 6.65 1.18 0.19 3.96 7.53
=============================================================
Special Error
=============================
Type FalsePos FalseNeg
-----------------------------
dAP 16.28 15.57
=============================
```
And a summary plot for your model's errors:
## Jupyter Notebook
Check out the [example notebook](https://github.com/dbolya/tide/blob/master/examples/coco_instance_segmentation.ipynb) for more details.
# Datasets
The currently supported datasets are COCO, LVIS, Pascal, and Cityscapes. More details and documentation on how to write your own database drivers coming soon!
# Citation
If you use TIDE in your project, please cite
```
@inproceedings{tide-eccv2020,
author = {Daniel Bolya and Sean Foley and James Hays and Judy Hoffman},
title = {TIDE: A General Toolbox for Identifying Object Detection Errors},
booktitle = {ECCV},
year = {2020},
}
```
## Contact
For questions about our paper or code, make an issue in this github or contact [Daniel Bolya](mailto:[email protected]). Note that I may not respond to emails, so github issues are your best bet.