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Commit c95a0e99 authored by André Anjos's avatar André Anjos :speech_balloon:
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[doc] Better tables with list-table directive; Fix all warnings

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doc/acknowledgements.rst
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.. -*- coding: utf-8 -*-
.. _bob.ip.binseg.acknowledgements:
================
Acknowledgements
================
==================
Acknowledgements
==================
This packages utilizes code from the following packages:
* The model-checkpointer is based on the Checkpointer in maskrcnn_benchmark by::
@misc{massa2018mrcnn,
author = {Massa, Francisco and Girshick, Ross},
title = {{maskrcnn-benchmark: Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch}},
year = {2018},
howpublished = {\url{https://github.com/facebookresearch/maskrcnn-benchmark}},
note = {Accessed: 2019.05.01}
}
* The AdaBound optimizer code by::
@inproceedings{Luo2019AdaBound,
author = {Luo, Liangchen and Xiong, Yuanhao and Liu, Yan and Sun, Xu},
title = {Adaptive Gradient Methods with Dynamic Bound of Learning Rate},
booktitle = {Proceedings of the 7th International Conference on Learning Representations},
month = {May},
year = {2019},
address = {New Orleans, Louisiana}
}
* The model-checkpointer is based on the implementation in
`maskrcnn-benchmark`_ by [MASSA-2018]_
* The AdaBound optimizer code was sourced from [LUO-2019]_
* The MobileNetV2 backbone is based on [LIN-2018]_
* The MobileNetV2 backbone is based on an implementation by::
@misc{tonylins,
author = {Ji Lin},
title = {pytorch-mobilenet-v2},
year = {2018}
howpublished = {\url{https://github.com/tonylins/pytorch-mobilenet-v2}},
note = {Accessed: 2019.05.01}
}
.. include:: links.rst
doc/api.rst
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......@@ -116,6 +116,8 @@ Models
bob.ip.binseg.configs.models.unet
.. _bob.ip.binseg.configs.datasets:
Datasets
========
......
doc/benchmarkresults.rst
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.. -*- coding: utf-8 -*-
.. _bob.ip.binseg.benchmarkresults:
.. _bob.ip.binseg.benchmarkresults:
==================
Benchmark Results
==================
===================
Benchmark Results
===================
F1 Scores
===========
F1 Scores (micro-level)
-----------------------
* Benchmark results for models: DRIU, HED, M2UNet and U-Net.
* Models are trained and tested on the same dataset using the train-test split as indicated in :ref:`bob.ip.binseg.datasets`
* Benchmark results for models: DRIU, HED, M2U-Net and U-Net.
* Models are trained and tested on the same dataset using the
train-test split as indicated in :ref:`bob.ip.binseg.configs.datasets` (i.e.,
these are *intra*-datasets tests)
* Standard-deviations across all test images are indicated in brakets
+--------------------------------------------+------------------------------------------------+---------------------------------------------+-------------------------------------------+----------------------------------------------+---------------------------------------------+
| F1 (std) | :ref:`bob.ip.binseg.configs.datasets.chasedb1` | :ref:`bob.ip.binseg.configs.datasets.drive` | :ref:`bob.ip.binseg.configs.datasets.hrf` | :ref:`bob.ip.binseg.configs.datasets.iostar` | :ref:`bob.ip.binseg.configs.datasets.stare` |
+--------------------------------------------+------------------------------------------------+---------------------------------------------+-------------------------------------------+----------------------------------------------+---------------------------------------------+
| :ref:`bob.ip.binseg.configs.models.driu` | `0.810 (0.021) <driu_chasedb1.pth_>`_ | `0.820 (0.014) <driu_drive.pth_>`_ | `0.783 (0.055) <driu_hrf.pth_>`_ | `0.825 (0.020) <driu_iostar.pth_>`_ | `0.827 (0.037) <driu_stare.pth_>`_ |
+--------------------------------------------+------------------------------------------------+---------------------------------------------+-------------------------------------------+----------------------------------------------+---------------------------------------------+
| :ref:`bob.ip.binseg.configs.models.hed` | 0.810 (0.022) | 0.817 (0.013) | 0.783 (0.058) | 0.825 (0.020) | 0.823 (0.037) |
+--------------------------------------------+------------------------------------------------+---------------------------------------------+-------------------------------------------+----------------------------------------------+---------------------------------------------+
| :ref:`bob.ip.binseg.configs.models.m2unet` | `0.802 (0.019) <m2unet_chasedb1.pth_>`_ | `0.803 (0.014) <m2unet_drive.pth_>`_ | `0.780 (0.057) <m2unet_hrf.pth_>`_ | `0.817 (0.020) <m2unet_iostar.pth_>`_ | `0.815 (0.041) <m2unet_stare.pth_>`_ |
+--------------------------------------------+------------------------------------------------+---------------------------------------------+-------------------------------------------+----------------------------------------------+---------------------------------------------+
| :ref:`bob.ip.binseg.configs.models.unet` | 0.812 (0.020) | 0.822 (0.015) | 0.788 (0.051) | 0.818 (0.019) | 0.829 (0.042) |
+--------------------------------------------+------------------------------------------------+---------------------------------------------+-------------------------------------------+----------------------------------------------+---------------------------------------------+
* Database and Model links (table top row and left column) are linked to the
originating configuration files used to obtain these results.
* For some results, the actual deep neural network models are provided (by
clicking on the associated F1 Score).
* Check `our paper`_ for details on the calculation of the F1 Score and standard
deviations.
.. list-table::
:header-rows: 1
* - F1 (std)
- :py:mod:`DRIU <bob.ip.binseg.configs.models.driu>`
- :py:mod:`HED <bob.ip.binseg.configs.models.hed>`
- :py:mod:`M2U-Net <bob.ip.binseg.configs.models.m2unet>`
- :py:mod:`U-Net <bob.ip.binseg.configs.models.unet>`
* - :py:mod:`CHASE-DB1 <bob.ip.binseg.configs.datasets.chasedb1>`
- `0.810 (0.021) <driu_chasedb1.pth_>`_
- 0.810 (0.022)
- `0.802 (0.019) <m2unet_chasedb1.pth_>`_
- 0.812 (0.020)
* - :py:mod:`DRIVE <bob.ip.binseg.configs.datasets.drive>`
- `0.820 (0.014) <driu_drive.pth_>`_
- 0.817 (0.013)
- `0.803 (0.014) <m2unet_drive.pth_>`_
- 0.822 (0.015)
* - :py:mod:`HRF <bob.ip.binseg.configs.datasets.hrf1168>`
- `0.783 (0.055) <driu_hrf.pth_>`_
- 0.783 (0.058)
- `0.780 (0.057) <m2unet_hrf.pth_>`_
- 0.788 (0.051)
* - :py:mod:`IOSTAR (vessel) <bob.ip.binseg.configs.datasets.iostarvessel>`
- `0.825 (0.020) <driu_iostar.pth_>`_
- 0.825 (0.020)
- `0.817 (0.020) <m2unet_iostar.pth_>`_
- 0.818 (0.019)
* - :py:mod:`STARE <bob.ip.binseg.configs.datasets.stare>`
- `0.827 (0.037) <driu_stare.pth_>`_
- 0.823 (0.037)
- `0.815 (0.041) <m2unet_stare.pth_>`_
- 0.829 (0.042)
.. include:: links.rst
doc/configs.rst deleted 100644 → 0
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.. -*- coding: utf-8 -*-
.. _bob.ip.binseg.configs:
===============
Configs
===============
Dataset Configs
===============
We provide variants for the training and test sets of each supported database,
as well as versions for COVD- (COmbined training sets of all publicly available
Vessel Dataset without target dataset) and SSL (Semi-supervised Learning), as
explained in our report.
.. _bob.ip.binseg.configs.datasets.imagefolder:
ImageFolder
-----------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/imagefolder.py
.. _bob.ip.binseg.configs.datasets.imagefoldertest:
ImageFolderTest
---------------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/imagefoldertest.py
.. _bob.ip.binseg.configs.datasets.imagefolderinference:
ImageFolderInference
--------------------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/imagefolderinference.py
.. _bob.ip.binseg.configs.datasets.chasedb1:
CHASEDB1
--------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/chasedb1.py
.. _bob.ip.binseg.configs.datasets.chasedb1test:
CHASEDB1TEST
------------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/chasedb1test.py
.. _bob.ip.binseg.configs.datasets.covd-drive:
COVD-DRIVE
----------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/starechasedb1iostarhrf544.py
.. _bob.ip.binseg.configs.datasets.covd-drive_ssl:
COVD-DRIVE_SSL
--------------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/starechasedb1iostarhrf544ssldrive.py
.. _bob.ip.binseg.configs.datasets.covd-stare:
COVD-STARE
----------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608.py
.. _bob.ip.binseg.configs.datasets.covd-stare_ssl:
COVD-STARE_SSL
--------------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608sslstare.py
.. _bob.ip.binseg.configs.datasets.covd-iostar:
COVD-IOSTARVESSEL
-----------------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/drivestarechasedb1hrf1024.py
.. _bob.ip.binseg.configs.datasets.covd-iostar_ssl:
COVD-IOSTARVESSEL_SSL
---------------------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/drivestarechasedb1hrf1024ssliostar.py
.. _bob.ip.binseg.configs.datasets.covd-hrf:
COVD-HRF
--------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/drivestarechasedb1iostar1168.py
.. _bob.ip.binseg.configs.datasets.covd-hrf_ssl:
COVD-HRF_SSL
------------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/drivestarechasedb1iostar1168sslhrf.py
.. _bob.ip.binseg.configs.datasets.covd-chasedb1:
COVD-CHASEDB1
-------------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/drivestareiostarhrf960.py
.. _bob.ip.binseg.configs.datasets.covd-chasedb1_ssl:
COVD-CHASEDB1_SSL
-----------------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/drivestareiostarhrf960.py
.. _bob.ip.binseg.configs.datasets.drive:
DRIVE
-----
.. literalinclude:: ../bob/ip/binseg/configs/datasets/drive.py
.. _bob.ip.binseg.configs.datasets.drivetest:
DRIVETEST
---------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/drivetest.py
.. _bob.ip.binseg.configs.datasets.hrf:
HRF
---
.. literalinclude:: ../bob/ip/binseg/configs/datasets/hrf1168.py
.. _bob.ip.binseg.configs.datasets.hrftest:
HRFTEST
-------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/hrftest.py
.. _bob.ip.binseg.configs.datasets.iostar:
IOSTARVESSEL
------------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/iostarvessel.py
.. _bob.ip.binseg.configs.datasets.iostarvesseltest:
IOSTARVESSELTEST
----------------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/iostarvesseltest.py
.. _bob.ip.binseg.configs.datasets.stare:
STARE
-----
.. literalinclude:: ../bob/ip/binseg/configs/datasets/stare.py
.. _bob.ip.binseg.configs.datasets.staretest:
STARETEST
---------
.. literalinclude:: ../bob/ip/binseg/configs/datasets/staretest.py
Model Configs
==============
.. _bob.ip.binseg.configs.models.driu:
DRIU
----
.. literalinclude:: ../bob/ip/binseg/configs/models/driu.py
.. _bob.ip.binseg.configs.models.driubn:
DRIUBN
------
.. literalinclude:: ../bob/ip/binseg/configs/models/driubn.py
.. _bob.ip.binseg.configs.models.hed:
HED
---
.. literalinclude:: ../bob/ip/binseg/configs/models/hed.py
.. _bob.ip.binseg.configs.models.m2unet:
M2UNet
------
.. literalinclude:: ../bob/ip/binseg/configs/models/m2unet.py
.. _bob.ip.binseg.configs.models.unet:
UNet
----
.. literalinclude:: ../bob/ip/binseg/configs/models/unet.py
.. _bob.ip.binseg.configs.models.driussl:
DRIUSSL
-------
.. literalinclude:: ../bob/ip/binseg/configs/models/driussl.py
.. _bob.ip.binseg.configs.models.driubnssl:
DRIUBNSSL
---------
.. literalinclude:: ../bob/ip/binseg/configs/models/driubnssl.py
.. _bob.ip.binseg.configs.models.m2unetssl:
M2UNetSSL
---------
.. literalinclude:: ../bob/ip/binseg/configs/models/m2unetssl.py
doc/covdresults.rst
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============================
In addition to the M2U-Net architecture, we also evaluated the larger DRIU
network and a variation of it that contains batch normalization (DRIU BN) on
COVD- and COVD-SSL. Perhaps surprisingly, for the majority of combinations, the
performance of the DRIU variants are roughly equal or worse than the M2U-Net.
We anticipate that one reason for this could be overparameterization of large
VGG16 models that are pretrained on ImageNet.
network and a variation of it that contains batch normalization (DRIU+BN) on
COVD- (Combined Vessel Dataset from all training data minus target test set)
and COVD-SSL (COVD- and Semi-Supervised Learning). Perhaps surprisingly, for
the majority of combinations, the performance of the DRIU variants are roughly
equal or worse to the ones obtained with the much smaller M2U-Net. We
anticipate that one reason for this could be overparameterization of large
VGG-16 models that are pretrained on ImageNet.
F1 Scores
=========
Comparison of F1-micro-scores (std) of DRIU and M2U-Net on COVD- and COVD-SSL.
Standard deviation across test-images in brackets.
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+
| F1 score | :ref:`bob.ip.binseg.configs.models.driu`/:ref:`bob.ip.binseg.configs.models.driussl` | :ref:`bob.ip.binseg.configs.models.driubn`/:ref:`bob.ip.binseg.configs.models.driubnssl` | :ref:`bob.ip.binseg.configs.models.m2unet`/:ref:`bob.ip.binseg.configs.models.m2unetssl` |
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+
| :ref:`bob.ip.binseg.configs.datasets.covd-drive` | 0.788 (0.018) | 0.797 (0.019) | `0.789 (0.018) <m2unet_covd-drive.pth>`_ |
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+
| :ref:`bob.ip.binseg.configs.datasets.covd-drive_ssl` | 0.785 (0.018) | 0.783 (0.019) | `0.791 (0.014) <m2unet_covd-drive_ssl.pth>`_ |
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+
| :ref:`bob.ip.binseg.configs.datasets.covd-stare` | 0.778 (0.117) | 0.778 (0.122) | `0.812 (0.046) <m2unet_covd-stare.pth>`_ |
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+
| :ref:`bob.ip.binseg.configs.datasets.covd-stare_ssl` | 0.788 (0.102) | 0.811 (0.074) | `0.820 (0.044) <m2unet_covd-stare_ssl.pth>`_ |
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+
| :ref:`bob.ip.binseg.configs.datasets.covd-chasedb1` | 0.796 (0.027) | 0.791 (0.025) | `0.788 (0.024) <m2unet_covd-chasedb1.pth>`_ |
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+
| :ref:`bob.ip.binseg.configs.datasets.covd-chasedb1_ssl` | 0.796 (0.024) | 0.798 (0.025) | `0.799 (0.026) <m2unet_covd-chasedb1_ssl.pth>`_ |
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+
| :ref:`bob.ip.binseg.configs.datasets.covd-hrf` | 0.799 (0.044) | 0.800 (0.045) | `0.802 (0.045) <m2unet_covd-hrf.pth>`_ |
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+
| :ref:`bob.ip.binseg.configs.datasets.covd-hrf_ssl` | 0.799 (0.044) | 0.784 (0.048) | `0.797 (0.044) <m2unet_covd-hrf_ssl.pth>`_ |
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+
| :ref:`bob.ip.binseg.configs.datasets.covd-iostar` | 0.791 (0.021) | 0.777 (0.032) | `0.793 (0.015) <m2unet_covd-iostar.pth>`_ |
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+
| :ref:`bob.ip.binseg.configs.datasets.covd-iostar_ssl` | 0.797 (0.017) | 0.811 (0.074) | `0.785 (0.018) <m2unet_covd-iostar_ssl.pth>`_ |
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+
---------
Comparison of F1 Scores (micro-level and standard deviation) of DRIU and
M2U-Net on COVD- and COVD-SSL. Standard deviation across test-images in
brackets.
.. list-table::
:header-rows: 1
* - F1 score
- :py:mod:`DRIU <bob.ip.binseg.configs.models.driu>`/:py:mod:`DRIU@SSL <bob.ip.binseg.configs.models.driussl>`
- :py:mod:`DRIU+BN <bob.ip.binseg.configs.models.driubn>`/:py:mod:`DRIU+BN@SSL <bob.ip.binseg.configs.models.driubnssl>`
- :py:mod:`M2U-Net <bob.ip.binseg.configs.models.m2unet>`/:py:mod:`M2U-Net@SSL <bob.ip.binseg.configs.models.m2unetssl>`
* - :py:mod:`DRIVE (COVD-) <bob.ip.binseg.configs.datasets.starechasedb1iostarhrf544>`
- 0.788 (0.018)
- 0.797 (0.019)
- `0.789 (0.018) <m2unet_covd-drive.pth>`_
* - :py:mod:`DRIVE (SSL, COVD-) <bob.ip.binseg.configs.datasets.starechasedb1iostarhrf544ssldrive>`
- 0.785 (0.018)
- 0.783 (0.019)
- `0.791 (0.014) <m2unet_covd-drive_ssl.pth>`_
* - :py:mod:`STARE (COVD-) <bob.ip.binseg.configs.datasets.drivechasedb1iostarhrf608>`
- 0.778 (0.117)
- 0.778 (0.122)
- `0.812 (0.046) <m2unet_covd-stare.pth>`_
* - :py:mod:`STARE (SSL, COVD-) <bob.ip.binseg.configs.datasets.drivechasedb1iostarhrf608sslstare>`
- 0.788 (0.102)
- 0.811 (0.074)
- `0.820 (0.044) <m2unet_covd-stare_ssl.pth>`_
* - :py:mod:`CHASE-DB1 (COVD-) <bob.ip.binseg.configs.datasets.drivestareiostarhrf960>`
- 0.796 (0.027)
- 0.791 (0.025)
- `0.788 (0.024) <m2unet_covd-chasedb1.pth>`_
* - :py:mod:`CHASE-DB1 (SSL, COVD-) <bob.ip.binseg.configs.datasets.drivestareiostarhrf960sslchase>`
- 0.796 (0.024)
- 0.798 (0.025)
- `0.799 (0.026) <m2unet_covd-chasedb1_ssl.pth>`_
* - :py:mod:`HRF (COVD-) <bob.ip.binseg.configs.datasets.drivestarechasedb1iostar1168>`
- 0.799 (0.044)
- 0.800 (0.045)
- `0.802 (0.045) <m2unet_covd-hrf.pth>`_
* - :py:mod:`HRF (SSL, COVD-) <bob.ip.binseg.configs.datasets.drivestarechasedb1iostar1168sslhrf>`
- 0.799 (0.044)
- 0.784 (0.048)
- `0.797 (0.044) <m2unet_covd-hrf_ssl.pth>`_
* - :py:mod:`IOSTAR (vessel, COVD-) <bob.ip.binseg.configs.datasets.drivestarechasedb1hrf1024>`
- 0.791 (0.021)
- 0.777 (0.032)
- `0.793 (0.015) <m2unet_covd-iostar.pth>`_
* - :py:mod:`IOSTAR (vessel, SSL, COVD-) <bob.ip.binseg.configs.datasets.drivestarechasedb1hrf1024ssliostar>`
- 0.797 (0.017)
- 0.811 (0.074)
- `0.785 (0.018) <m2unet_covd-iostar_ssl.pth>`_
M2U-Net Precision vs. Recall Curves
===================================
-----------------------------------
Precision vs. recall curves for each evaluated dataset. Note that here the
F1-score is calculated on a macro level (see paper for more details).
......
doc/datasets.rst
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......@@ -12,29 +12,141 @@ can be downloaded. We include the reference of the data split protocols used
to generate iterators for training and testing.
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+
| Dataset | Reference | ``bob.db`` package | H x W | Samples | Mask | Vessel | OD | Cup | Split Reference | Train | Test |
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+
| DRIVE_ | [DRIVE-2004]_ | ``bob.db.drive`` | 584 x 565 | 40 | x | x | | | [DRIVE-2004]_ | 20 | 20 |
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+
| STARE_ | [STARE-2000]_ | ``bob.db.stare`` | 605 x 700 | 20 | | x | | | [MANINIS-2016]_ | 10 | 10 |
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+
| CHASE-DB1_ | [CHASEDB1-2012]_ | ``bob.db.chasedb`` | 960 x 999 | 28 | | x | | | [CHASEDB1-2012]_ | 8 | 20 |
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+
| HRF_ | [HRF-2013]_ | ``bob.db.hrf`` | 2336 x 3504 | 45 | x | x | | | [ORLANDO-2017]_ | 15 | 30 |
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+
| IOSTAR_ | [IOSTAR-2016]_ | ``bob.db.iostar`` | 1024 x 1024 | 30 | x | x | x | | [MEYER-2017]_ | 20 | 10 |
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+
| DRIONS-DB_ | [DRIONSDB-2008]_ | ``bob.db.drionsdb`` | 400 x 600 | 110 | | | x | | [MANINIS-2016]_ | 60 | 50 |
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+
| `RIM-ONE r3`_ | [RIMONER3-2015]_ | ``bob.db.rimoner3`` | 1424 x 1072 | 159 | | | x | x | [MANINIS-2016]_ | 99 | 60 |
+-----------------+-------------------+------------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+
| Drishti-GS1_ | [DRISHTIGS1-2014]_ | ``bob.db.drishtigs1`` | varying | 101 | | | x | x | [DRISHTIGS1-2014]_ | 50 | 51 |
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+
| REFUGE_ (train) | [REFUGE-2018]_ | ``bob.db.refuge`` | 2056 x 2124 | 400 | | | x | x | [REFUGE-2018]_ | 400 | |
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+
| REFUGE_ (val) | [REFUGE-2018]_ | ``bob.db.refuge`` | 1634 x 1634 | 400 | | | x | x | [REFUGE-2018]_ | | 400 |
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+
.. list-table::
:header-rows: 1
* - Dataset
- Reference
- ``bob.db`` package
- H x W
- Samples
- Mask
- Vessel
- OD
- Cup
- Split Reference
- Train
- Test
* - DRIVE_
- [DRIVE-2004]_
- ``bob.db.drive``
- 584 x 565
- 40
- x
- x
-
-
- [DRIVE-2004]_
- 20
- 20
* - STARE_
- [STARE-2000]_
- ``bob.db.stare``
- 605 x 700
- 20
-
- x
-
-
- [MANINIS-2016]_
- 10
- 10
* - CHASE-DB1_
- [CHASEDB1-2012]_
- ``bob.db.chasedb``
- 960 x 999
- 28
-
- x
-
-
- [CHASEDB1-2012]_
- 8
- 20
* - HRF_
- [HRF-2013]_
- ``bob.db.hrf``
- 2336 x 3504
- 45
- x
- x
-
-
- [ORLANDO-2017]_
- 15
- 30
* - IOSTAR_
- [IOSTAR-2016]_
- ``bob.db.iostar``
- 1024 x 1024
- 30
- x
- x
- x
-
- [MEYER-2017]_
- 20
- 10
* - DRIONS-DB_
- [DRIONSDB-2008]_
- ``bob.db.drionsdb``
- 400 x 600
- 110
-
-
- x
-
- [MANINIS-2016]_
- 60
- 50
* - `RIM-ONE r3`_
- [RIMONER3-2015]_
- ``bob.db.rimoner3``
- 1424 x 1072
- 159
-
-
- x
- x
- [MANINIS-2016]_
- 99
- 60
* - Drishti-GS1_
- [DRISHTIGS1-2014]_
- ``bob.db.drishtigs1``
- varying
- 101
-
-
- x
- x
- [DRISHTIGS1-2014]_
- 50
- 51
* - REFUGE_ (train)
- [REFUGE-2018]_
- ``bob.db.refuge``
- 2056 x 2124
- 400
-
-
- x
- x
- [REFUGE-2018]_
- 400
-
* - REFUGE_ (val)
- [REFUGE-2018]_
- ``bob.db.refuge``
- 1634 x 1634
- 400
-
-
- x
- x
- [REFUGE-2018]_
-
- 400
Folder-based Dataset
......@@ -55,7 +167,7 @@ be read via PIL are supported. Additionally, we also support HDF5 binary
files.
For training, a new dataset configuration needs to be created. You can copy the
template :ref:`bob.ip.binseg.configs.datasets.imagefolder` and amend it
template :py:mod:`bob.ip.binseg.configs.datasets.imagefolder` and amend it
accordingly, e.g. to point to the the full path of the dataset and if necessary
any preprocessing steps such as resizing, cropping, padding etc.
......@@ -66,8 +178,8 @@ Training can then be started with, e.g.:
bob binseg train M2UNet /path/to/myimagefolderconfig.py -b 4 -d cuda -o /my/output/path -vv
Similary for testing, a test dataset config needs to be created. You can copy
the template :ref:`bob.ip.binseg.configs.datasets.imagefoldertest` and amend it
accordingly.
the template :py:mod:`bob.ip.binseg.configs.datasets.imagefoldertest` and amend
it accordingly.
Testing can then be started with, e.g.:
......
doc/evaluation.rst
+ 6
− 4
View file @ c95a0e99
......@@ -33,10 +33,10 @@ The inference run generates the following output files:
.. code-block:: bash
.
├── images # the predicted probabilities as grayscale images in .png format
├── images # the predicted probabilities as grayscale images in .png format
├── hdf5 # the predicted probabilties in hdf5 format
├── last_checkpoint # text file that keeps track of the last checkpoint
├── M2UNet_trainlog.csv # training log
├── last_checkpoint # text file that keeps track of the last checkpoint
├── M2UNet_trainlog.csv # training log
├── M2UNet_trainlog.pdf # training log plot
├── model_*.pth # model checkpoints
└── results
......@@ -49,7 +49,9 @@ The inference run generates the following output files:
Inference Only Mode
====================
If you wish to run inference only on a folder containing images, use the ``predict`` function in combination with a :ref:`bob.ip.binseg.configs.datasets.imagefolderinference` config. E.g.:
If you wish to run inference only on a folder containing images, use the
``predict`` function in combination with a
:py:mod:`bob.ip.binseg.configs.datasets.imagefolderinference` config. E.g.:
.. code-block:: bash
......
doc/index.rst
+ 0
− 1
View file @ c95a0e99
......@@ -47,7 +47,6 @@ Users Guide
evaluation
benchmarkresults
covdresults
configs
plotting
visualization
acknowledgements
......
doc/links.rst
+ 1
− 0
View file @ c95a0e99
......@@ -7,6 +7,7 @@
.. _installation: https://www.idiap.ch/software/bob/install
.. _mailing list: https://www.idiap.ch/software/bob/discuss
.. _pytorch: https://pytorch.org
.. _our paper: https://arxiv.org/abs/1909.03856
.. Raw data websites
.. _drive: https://www.isi.uu.nl/Research/Databases/DRIVE/
......
doc/references.rst
+ 14
− 0
View file @ c95a0e99
......@@ -75,3 +75,17 @@
.. [HE-2015] *S. Xie and Z. Tu*, **Holistically-Nested Edge Detection**, 2015
IEEE International Conference on Computer Vision (ICCV), Santiago, 2015, pp.
1395-1403. https://doi.org/10.1109/ICCV.2015.164
.. [LUO-2019] *L. Luo, Y. Xiong, Y. Liu, and X. Sun*, **Adaptive Gradient
Methods with Dynamic Bound of Learning Rate**, Proceedings of the 7th
International Conference on Learning Representations (ICLR), Feb. 2019.
https://arxiv.org/abs/1902.09843v1
.. [MASSA-2018] *F. Massa and R. Girshick*, **maskrcnn-benchmark: Fast, modular
reference implementation of Instance Segmentation and Object Detection
algorithms in PyTorch**. 2018. Last accessed: 21.03.2020.
https://github.com/facebookresearch/maskrcnn-benchmark
.. [LIN-2018] *J. Lin*, **pytorch-mobilenet-v2: A PyTorch implementation of
MobileNetV2**, 2018. Last accessed: 21.03.2020.
https://github.com/tonylins/pytorch-mobilenet-v2
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