index.rst 4.13 KB
Newer Older
limm's avatar
limm committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
Welcome to MMPretrain's documentation!
============================================

MMPretrain is a newly upgraded open-source framework for pre-training.
It has set out to provide multiple powerful pre-trained backbones and
support different pre-training strategies. MMPretrain originated from the
famous open-source projects
`MMClassification <https://github.com/open-mmlab/mmclassification/tree/1.x>`_
and `MMSelfSup <https://github.com/open-mmlab/mmselfsup>`_, and is developed
with many exiciting new features. The pre-training stage is essential for
vision recognition currently. With the rich and strong pre-trained models,
we are currently capable of improving various downstream vision tasks.

Our primary objective for the codebase is to become an easily accessible and
user-friendly library and to streamline research and engineering. We
detail the properties and design of MMPretrain across different sections.

Hands-on Roadmap of MMPretrain
-------------------------------

To help users quickly utilize MMPretrain, we recommend following the hands-on
roadmap we have created for the library:

   - For users who want to try MMPretrain, we suggest reading the GetStarted_
     section for the environment setup.

   - For basic usage, we refer users to UserGuides_ for utilizing various
     algorithms to obtain the pre-trained models and evaluate their performance
     in downstream tasks.

   - For those who wish to customize their own algorithms, we provide
     AdvancedGuides_ that include hints and rules for modifying code.

   - To find your desired pre-trained models, users could check the ModelZoo_,
     which features a summary of various backbones and pre-training methods and
     introfuction of different algorithms.

   - Additionally, we provide Analysis_ and Visualization_ tools to help
     diagnose algorithms.

   - Besides, if you have any other questions or concerns, please refer to the
     Notes_ section for potential answers.

We always welcome *PRs* and *Issues* for the betterment of MMPretrain.

.. _GetStarted:
.. toctree::
   :maxdepth: 1
   :caption: Get Started

   get_started.md

.. _UserGuides:
.. toctree::
   :maxdepth: 1
   :caption: User Guides

   user_guides/config.md
   user_guides/dataset_prepare.md
   user_guides/inference.md
   user_guides/train.md
   user_guides/test.md
   user_guides/downstream.md

.. _AdvancedGuides:
.. toctree::
   :maxdepth: 1
   :caption: Advanced Guides

   advanced_guides/datasets.md
   advanced_guides/pipeline.md
   advanced_guides/modules.md
   advanced_guides/schedule.md
   advanced_guides/runtime.md
   advanced_guides/evaluation.md
   advanced_guides/convention.md

.. _ModelZoo:
.. toctree::
   :maxdepth: 1
   :caption: Model Zoo
   :glob:

   modelzoo_statistics.md
   papers/*

.. _Visualization:
.. toctree::
   :maxdepth: 1
   :caption: Visualization

   useful_tools/dataset_visualization.md
   useful_tools/scheduler_visualization.md
   useful_tools/cam_visualization.md
   useful_tools/t-sne_visualization.md

.. _Analysis:
.. toctree::
   :maxdepth: 1
   :caption: Analysis Tools

   useful_tools/print_config.md
   useful_tools/verify_dataset.md
   useful_tools/log_result_analysis.md
   useful_tools/complexity_analysis.md
   useful_tools/confusion_matrix.md
   useful_tools/shape_bias.md

.. toctree::
   :maxdepth: 1
   :caption: Deployment

   useful_tools/model_serving.md

.. toctree::
   :maxdepth: 1
   :caption: Migration

   migration.md

.. toctree::
   :maxdepth: 1
   :caption: API Reference

   mmpretrain.apis <api/apis>
   mmpretrain.engine <api/engine>
   mmpretrain.datasets <api/datasets>
   Data Process <api/data_process>
   mmpretrain.models <api/models>
   mmpretrain.structures <api/structures>
   mmpretrain.visualization <api/visualization>
   mmpretrain.evaluation <api/evaluation>
   mmpretrain.utils <api/utils>

.. _Notes:
.. toctree::
   :maxdepth: 1
   :caption: Notes

   notes/contribution_guide.md
   notes/projects.md
   notes/changelog.md
   notes/faq.md
   notes/pretrain_custom_dataset.md
   notes/finetune_custom_dataset.md

.. toctree::
   :maxdepth: 1
   :caption: Device Support

   device/npu.md

Indices and tables
==================

* :ref:`genindex`
* :ref:`search`