Commit d26e0f14 authored by Philip ABBET's avatar Philip ABBET

Add gbu/3 (api change: beat.backend.python v1.4.2)

parent 8ee4726a
{
"description": "The Good, the Bad and the Ugly Face Challenge",
"root_folder": "/idiap/resource/database/MBGC-V1",
"protocols": [
{
"name": "good",
"template": "simple_face_recognition_gbu",
"sets": [
{
"name": "train",
"template": "train",
"view": "Train",
"parameters": {
"protocol": "Good"
},
"outputs": {
"file_id": "{{ system_user.username }}/uint64/1",
"client_id": "{{ system_user.username }}/uint64/1",
"image": "{{ system_user.username }}/array_3d_uint8/1",
"eye_centers": "{{ system_user.username }}/eye_positions/1"
}
},
{
"name": "templates",
"template": "templates",
"view": "Templates",
"parameters": {
"protocol": "Good"
},
"outputs": {
"file_id": "{{ system_user.username }}/uint64/1",
"client_id": "{{ system_user.username }}/uint64/1",
"template_id": "{{ system_user.username }}/uint64/1",
"image": "{{ system_user.username }}/array_3d_uint8/1",
"eye_centers": "{{ system_user.username }}/eye_positions/1"
}
},
{
"name": "probes",
"template": "probes",
"view": "Probes",
"parameters": {
"protocol": "Good"
},
"outputs": {
"file_id": "{{ system_user.username }}/uint64/1",
"probe_id": "{{ system_user.username }}/uint64/1",
"client_id": "{{ system_user.username }}/uint64/1",
"template_ids": "{{ system_user.username }}/array_1d_uint64/1",
"image": "{{ system_user.username }}/array_3d_uint8/1",
"eye_centers": "{{ system_user.username }}/eye_positions/1"
}
}
]
},
{
"name": "bad",
"template": "simple_face_recognition_gbu",
"sets": [
{
"name": "train",
"template": "train",
"view": "Train",
"parameters": {
"protocol": "Bad"
},
"outputs": {
"file_id": "{{ system_user.username }}/uint64/1",
"client_id": "{{ system_user.username }}/uint64/1",
"image": "{{ system_user.username }}/array_3d_uint8/1",
"eye_centers": "{{ system_user.username }}/eye_positions/1"
}
},
{
"name": "templates",
"template": "templates",
"view": "Templates",
"parameters": {
"protocol": "Bad"
},
"outputs": {
"file_id": "{{ system_user.username }}/uint64/1",
"client_id": "{{ system_user.username }}/uint64/1",
"template_id": "{{ system_user.username }}/uint64/1",
"image": "{{ system_user.username }}/array_3d_uint8/1",
"eye_centers": "{{ system_user.username }}/eye_positions/1"
}
},
{
"name": "probes",
"template": "probes",
"view": "Probes",
"parameters": {
"protocol": "Bad"
},
"outputs": {
"file_id": "{{ system_user.username }}/uint64/1",
"probe_id": "{{ system_user.username }}/uint64/1",
"client_id": "{{ system_user.username }}/uint64/1",
"template_ids": "{{ system_user.username }}/array_1d_uint64/1",
"image": "{{ system_user.username }}/array_3d_uint8/1",
"eye_centers": "{{ system_user.username }}/eye_positions/1"
}
}
]
},
{
"name": "ugly",
"template": "simple_face_recognition_gbu",
"sets": [
{
"name": "train",
"template": "train",
"view": "Train",
"parameters": {
"protocol": "Ugly"
},
"outputs": {
"file_id": "{{ system_user.username }}/uint64/1",
"client_id": "{{ system_user.username }}/uint64/1",
"image": "{{ system_user.username }}/array_3d_uint8/1",
"eye_centers": "{{ system_user.username }}/eye_positions/1"
}
},
{
"name": "templates",
"template": "templates",
"view": "Templates",
"parameters": {
"protocol": "Ugly"
},
"outputs": {
"file_id": "{{ system_user.username }}/uint64/1",
"client_id": "{{ system_user.username }}/uint64/1",
"template_id": "{{ system_user.username }}/uint64/1",
"image": "{{ system_user.username }}/array_3d_uint8/1",
"eye_centers": "{{ system_user.username }}/eye_positions/1"
}
},
{
"name": "probes",
"template": "probes",
"view": "Probes",
"parameters": {
"protocol": "Ugly"
},
"outputs": {
"file_id": "{{ system_user.username }}/uint64/1",
"probe_id": "{{ system_user.username }}/uint64/1",
"client_id": "{{ system_user.username }}/uint64/1",
"template_ids": "{{ system_user.username }}/array_1d_uint64/1",
"image": "{{ system_user.username }}/array_3d_uint8/1",
"eye_centers": "{{ system_user.username }}/eye_positions/1"
}
}
]
}
]
}
This diff is collapsed.
.. Copyright (c) 2016 Idiap Research Institute, http://www.idiap.ch/ ..
.. Contact: beat.support@idiap.ch ..
.. ..
.. This file is part of the beat.examples module of the BEAT platform. ..
.. ..
.. Commercial License Usage ..
.. Licensees holding valid commercial BEAT licenses may use this file in ..
.. accordance with the terms contained in a written agreement between you ..
.. and Idiap. For further information contact tto@idiap.ch ..
.. ..
.. Alternatively, this file may be used under the terms of the GNU Affero ..
.. Public License version 3 as published by the Free Software and appearing ..
.. in the file LICENSE.AGPL included in the packaging of this file. ..
.. The BEAT platform is distributed in the hope that it will be useful, but ..
.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
.. or FITNESS FOR A PARTICULAR PURPOSE. ..
.. ..
.. You should have received a copy of the GNU Affero Public License along ..
.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
The Good, Bad and Ugly Database
-------------------------------
Changelog
=========
* **Version 3**, 31/Oct/2017:
- Port to beat.backend.python v1.4.2
* **Version 2**, 20/Jan/2016:
- Port to Bob v2
* **Version 1**, 08/Apr/2015:
- Initial release
Description
===========
`The Good, the Bad, and the Ugly challenge
<http://www.nist.gov/itl/iad/ig/focs.cfm>`_ consists of three frontal still
face partitions. The paritions were designed to encourage the development of
face recognition algorithms that excel at matching `hard` face pairs, but not
at the expense of performance on `easy` face pairs.
The images in this challenge problem are frontal face stills taken under
uncontrolled illumination, both indoors and outdoors. The three partitions
were constructed by analyzing results from the FRVT 2006. The Good set
consisted of face pairs that had above average performance, the Bad set
consisted of face pairs that had average performance, and the Ugly set
consisted of face pairs that had below average performance. There are 437
subjects in the data set. All three partitions have the same 437 subjects.
All three paritions have 1085 images in both the target and query sets.
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