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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 -*- .. -*- coding: utf-8 -*-
.. _bob.ip.binseg.acknowledgements: .. _bob.ip.binseg.acknowledgements:
================ ==================
Acknowledgements Acknowledgements
================ ==================
This packages utilizes code from the following packages: This packages utilizes code from the following packages:
* The model-checkpointer is based on the Checkpointer in maskrcnn_benchmark by:: * The model-checkpointer is based on the implementation in
`maskrcnn-benchmark`_ by [MASSA-2018]_
@misc{massa2018mrcnn, * The AdaBound optimizer code was sourced from [LUO-2019]_
author = {Massa, Francisco and Girshick, Ross}, * The MobileNetV2 backbone is based on [LIN-2018]_
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 MobileNetV2 backbone is based on an implementation by::
@misc{tonylins, .. include:: links.rst
author = {Ji Lin},
title = {pytorch-mobilenet-v2},
year = {2018}
howpublished = {\url{https://github.com/tonylins/pytorch-mobilenet-v2}},
note = {Accessed: 2019.05.01}
}
doc/api.rst
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...@@ -116,6 +116,8 @@ Models ...@@ -116,6 +116,8 @@ Models
bob.ip.binseg.configs.models.unet bob.ip.binseg.configs.models.unet
.. _bob.ip.binseg.configs.datasets:
Datasets Datasets
======== ========
......
doc/benchmarkresults.rst
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.. -*- coding: utf-8 -*- .. -*- 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. * 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.datasets` * 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 * Standard-deviations across all test images are indicated in brakets
* Database and Model links (table top row and left column) are linked to the
+--------------------------------------------+------------------------------------------------+---------------------------------------------+-------------------------------------------+----------------------------------------------+---------------------------------------------+ originating configuration files used to obtain these results.
| 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` | * For some results, the actual deep neural network models are provided (by
+--------------------------------------------+------------------------------------------------+---------------------------------------------+-------------------------------------------+----------------------------------------------+---------------------------------------------+ clicking on the associated F1 Score).
| :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_>`_ | * Check `our paper`_ for details on the calculation of the F1 Score and standard
+--------------------------------------------+------------------------------------------------+---------------------------------------------+-------------------------------------------+----------------------------------------------+---------------------------------------------+ deviations.
| :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) |
+--------------------------------------------+------------------------------------------------+---------------------------------------------+-------------------------------------------+----------------------------------------------+---------------------------------------------+ .. list-table::
| :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_>`_ | :header-rows: 1
+--------------------------------------------+------------------------------------------------+---------------------------------------------+-------------------------------------------+----------------------------------------------+---------------------------------------------+
| :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) | * - 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 .. 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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...@@ -7,44 +7,73 @@ ...@@ -7,44 +7,73 @@
============================ ============================
In addition to the M2U-Net architecture, we also evaluated the larger DRIU 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 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 COVD- (Combined Vessel Dataset from all training data minus target test set)
performance of the DRIU variants are roughly equal or worse than the M2U-Net. and COVD-SSL (COVD- and Semi-Supervised Learning). Perhaps surprisingly, for
We anticipate that one reason for this could be overparameterization of large the majority of combinations, the performance of the DRIU variants are roughly
VGG16 models that are pretrained on ImageNet. 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 F1 Scores
========= ---------
Comparison of F1-micro-scores (std) of DRIU and M2U-Net on COVD- and COVD-SSL. Comparison of F1 Scores (micro-level and standard deviation) of DRIU and
Standard deviation across test-images in brackets. 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` | .. list-table::
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+ :header-rows: 1
| :ref:`bob.ip.binseg.configs.datasets.covd-drive` | 0.788 (0.018) | 0.797 (0.019) | `0.789 (0.018) <m2unet_covd-drive.pth>`_ |
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+ * - F1 score
| :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>`_ | - :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>`
| :ref:`bob.ip.binseg.configs.datasets.covd-stare` | 0.778 (0.117) | 0.778 (0.122) | `0.812 (0.046) <m2unet_covd-stare.pth>`_ | - :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>`
| :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>`_ | - 0.788 (0.018)
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+ - 0.797 (0.019)
| :ref:`bob.ip.binseg.configs.datasets.covd-chasedb1` | 0.796 (0.027) | 0.791 (0.025) | `0.788 (0.024) <m2unet_covd-chasedb1.pth>`_ | - `0.789 (0.018) <m2unet_covd-drive.pth>`_
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+ * - :py:mod:`DRIVE (SSL, COVD-) <bob.ip.binseg.configs.datasets.starechasedb1iostarhrf544ssldrive>`
| :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>`_ | - 0.785 (0.018)
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+ - 0.783 (0.019)
| :ref:`bob.ip.binseg.configs.datasets.covd-hrf` | 0.799 (0.044) | 0.800 (0.045) | `0.802 (0.045) <m2unet_covd-hrf.pth>`_ | - `0.791 (0.014) <m2unet_covd-drive_ssl.pth>`_
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+ * - :py:mod:`STARE (COVD-) <bob.ip.binseg.configs.datasets.drivechasedb1iostarhrf608>`
| :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>`_ | - 0.778 (0.117)
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+ - 0.778 (0.122)
| :ref:`bob.ip.binseg.configs.datasets.covd-iostar` | 0.791 (0.021) | 0.777 (0.032) | `0.793 (0.015) <m2unet_covd-iostar.pth>`_ | - `0.812 (0.046) <m2unet_covd-stare.pth>`_
+---------------------------------------------------------+--------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+ * - :py:mod:`STARE (SSL, COVD-) <bob.ip.binseg.configs.datasets.drivechasedb1iostarhrf608sslstare>`
| :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>`_ | - 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 M2U-Net Precision vs. Recall Curves
=================================== -----------------------------------
Precision vs. recall curves for each evaluated dataset. Note that here the 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). 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 ...@@ -12,29 +12,141 @@ can be downloaded. We include the reference of the data split protocols used
to generate iterators for training and testing. to generate iterators for training and testing.
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+ .. list-table::
| Dataset | Reference | ``bob.db`` package | H x W | Samples | Mask | Vessel | OD | Cup | Split Reference | Train | Test | :header-rows: 1
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+
| DRIVE_ | [DRIVE-2004]_ | ``bob.db.drive`` | 584 x 565 | 40 | x | x | | | [DRIVE-2004]_ | 20 | 20 | * - Dataset
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+ - Reference
| STARE_ | [STARE-2000]_ | ``bob.db.stare`` | 605 x 700 | 20 | | x | | | [MANINIS-2016]_ | 10 | 10 | - ``bob.db`` package
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+ - H x W
| CHASE-DB1_ | [CHASEDB1-2012]_ | ``bob.db.chasedb`` | 960 x 999 | 28 | | x | | | [CHASEDB1-2012]_ | 8 | 20 | - Samples
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+ - Mask
| HRF_ | [HRF-2013]_ | ``bob.db.hrf`` | 2336 x 3504 | 45 | x | x | | | [ORLANDO-2017]_ | 15 | 30 | - Vessel
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+ - OD
| IOSTAR_ | [IOSTAR-2016]_ | ``bob.db.iostar`` | 1024 x 1024 | 30 | x | x | x | | [MEYER-2017]_ | 20 | 10 | - Cup
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+ - Split Reference
| DRIONS-DB_ | [DRIONSDB-2008]_ | ``bob.db.drionsdb`` | 400 x 600 | 110 | | | x | | [MANINIS-2016]_ | 60 | 50 | - Train
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+ - Test
| `RIM-ONE r3`_ | [RIMONER3-2015]_ | ``bob.db.rimoner3`` | 1424 x 1072 | 159 | | | x | x | [MANINIS-2016]_ | 99 | 60 | * - DRIVE_
+-----------------+-------------------+------------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+ - [DRIVE-2004]_
| Drishti-GS1_ | [DRISHTIGS1-2014]_ | ``bob.db.drishtigs1`` | varying | 101 | | | x | x | [DRISHTIGS1-2014]_ | 50 | 51 | - ``bob.db.drive``
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+ - 584 x 565
| REFUGE_ (train) | [REFUGE-2018]_ | ``bob.db.refuge`` | 2056 x 2124 | 400 | | | x | x | [REFUGE-2018]_ | 400 | | - 40
+-----------------+--------------------+-----------------------+-------------+---------+------+--------+-----+-----+--------------------+-------+------+ - x
| REFUGE_ (val) | [REFUGE-2018]_ | ``bob.db.refuge`` | 1634 x 1634 | 400 | | | x | x | [REFUGE-2018]_ | | 400 | - 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 Folder-based Dataset
...@@ -55,7 +167,7 @@ be read via PIL are supported. Additionally, we also support HDF5 binary ...@@ -55,7 +167,7 @@ be read via PIL are supported. Additionally, we also support HDF5 binary
files. files.
For training, a new dataset configuration needs to be created. You can copy the 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 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. any preprocessing steps such as resizing, cropping, padding etc.
...@@ -66,8 +178,8 @@ Training can then be started with, e.g.: ...@@ -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 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 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 the template :py:mod:`bob.ip.binseg.configs.datasets.imagefoldertest` and amend
accordingly. it accordingly.
Testing can then be started with, e.g.: 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: ...@@ -33,10 +33,10 @@ The inference run generates the following output files:
.. code-block:: bash .. 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 ├── hdf5 # the predicted probabilties in hdf5 format
├── last_checkpoint # text file that keeps track of the last checkpoint ├── last_checkpoint # text file that keeps track of the last checkpoint
├── M2UNet_trainlog.csv # training log ├── M2UNet_trainlog.csv # training log
├── M2UNet_trainlog.pdf # training log plot ├── M2UNet_trainlog.pdf # training log plot
├── model_*.pth # model checkpoints ├── model_*.pth # model checkpoints
└── results └── results
...@@ -49,7 +49,9 @@ The inference run generates the following output files: ...@@ -49,7 +49,9 @@ The inference run generates the following output files:
Inference Only Mode 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 .. code-block:: bash
......
doc/index.rst
+ 0
− 1
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...@@ -47,7 +47,6 @@ Users Guide ...@@ -47,7 +47,6 @@ Users Guide
evaluation evaluation
benchmarkresults benchmarkresults
covdresults covdresults
configs
plotting plotting
visualization visualization
acknowledgements acknowledgements
......
doc/links.rst
+ 1
− 0
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...@@ -7,6 +7,7 @@ ...@@ -7,6 +7,7 @@
.. _installation: https://www.idiap.ch/software/bob/install .. _installation: https://www.idiap.ch/software/bob/install
.. _mailing list: https://www.idiap.ch/software/bob/discuss .. _mailing list: https://www.idiap.ch/software/bob/discuss
.. _pytorch: https://pytorch.org .. _pytorch: https://pytorch.org
.. _our paper: https://arxiv.org/abs/1909.03856
.. Raw data websites .. Raw data websites
.. _drive: https://www.isi.uu.nl/Research/Databases/DRIVE/ .. _drive: https://www.isi.uu.nl/Research/Databases/DRIVE/
......
doc/references.rst
+ 14
− 0
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...@@ -75,3 +75,17 @@ ...@@ -75,3 +75,17 @@
.. [HE-2015] *S. Xie and Z. Tu*, **Holistically-Nested Edge Detection**, 2015 .. [HE-2015] *S. Xie and Z. Tu*, **Holistically-Nested Edge Detection**, 2015
IEEE International Conference on Computer Vision (ICCV), Santiago, 2015, pp. IEEE International Conference on Computer Vision (ICCV), Santiago, 2015, pp.
1395-1403. https://doi.org/10.1109/ICCV.2015.164 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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