Metadata-Version: 2.1
Name: tidecv
Version: 1.0.1
Summary: A General Toolbox for Identifying ObjectDetection Errors
Home-page: https://github.com/dbolya/tide
Author: Daniel Bolya
Author-email: dbolya@gatech.edu
License: MIT
Description: # A General **T**oolbox for **I**dentifying Object **D**etection **E**rrors
        ```
        ████████╗██╗██████╗ ███████╗
        ╚══██╔══╝██║██╔══██╗██╔════╝
           ██║   ██║██║  ██║█████╗  
           ██║   ██║██║  ██║██╔══╝  
           ██║   ██║██████╔╝███████╗
           ╚═╝   ╚═╝╚═════╝ ╚══════╝
        ```
        
        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:
        
        [![TIDE Introduction](https://img.youtube.com/vi/McYFYU3PXcU/0.jpg)](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:
        
        ![A summary plot](https://dbolya.github.io/tide/mask_rcnn_bbox_bbox_summary.png)
        
        ## 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:dbolya@gatech.edu). Note that I may not respond to emails, so github issues are your best bet.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
