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Fix documentation links (#972)
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<!-- Contributing guide:
https://github.com/openvinotoolkit/datumaro/blob/develop/CONTRIBUTING.md
-->

### Summary
- Fix broken documentation links

<!--
Resolves #111 and #222.
Depends on #1000 (for series of dependent commits).

This PR introduces this capability to make the project better in this
and that.

- Added this feature
- Removed that feature
- Fixed the problem #1234
-->

### How to test
<!-- Describe the testing procedure for reviewers, if changes are
not fully covered by unit tests or manual testing can be complicated.
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### Checklist
<!-- Put an 'x' in all the boxes that apply -->
- [ ] I have added unit tests to cover my changes.​
- [ ] I have added integration tests to cover my changes.​
- [ ] I have added the description of my changes into
[CHANGELOG](https://github.com/openvinotoolkit/datumaro/blob/develop/CHANGELOG.md).​
- [ ] I have updated the
[documentation](https://github.com/openvinotoolkit/datumaro/tree/develop/docs)
accordingly

### License

- [ ] I submit _my code changes_ under the same [MIT
License](https://github.com/openvinotoolkit/datumaro/blob/develop/LICENSE)
that covers the project.
  Feel free to contact the maintainers if that's a concern.
- [ ] I have updated the license header for each file (see an example
below).

```python
# Copyright (C) 2023 Intel Corporation
#
# SPDX-License-Identifier: MIT
```
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JihwanEom authored Apr 21, 2023
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17 changes: 9 additions & 8 deletions README.md
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Expand Up @@ -15,10 +15,11 @@ CVAT annotations ---> Publication, statistics etc.
```
<!--lint enable fenced-code-flag-->

- [Getting started](https://openvinotoolkit.github.io/datumaro/docs/getting_started)
- [Getting started](https://openvinotoolkit.github.io/datumaro/latest/docs/get-started/quick-start-guide)
- [Level Up](https://openvinotoolkit.github.io/datumaro/latest/docs/level-up/basic_skills)
- [Features](#features)
- [User manual](https://openvinotoolkit.github.io/datumaro/docs/user-manual)
- [Developer manual](https://openvinotoolkit.github.io/datumaro/api)
- [User manual](https://openvinotoolkit.github.io/datumaro/latest/docs/user-manual/how_to_use_datumaro)
- [Developer manual](https://openvinotoolkit.github.io/datumaro/latest/docs/reference/datumaro/datumaro)
- [Contributing](#contributing)

## Features
Expand All @@ -45,7 +46,7 @@ CVAT annotations ---> Publication, statistics etc.
(`bboxes`, `masks`)
- [YOLO](https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data) (`bboxes`)

Other formats and documentation for them can be found [here](https://openvinotoolkit.github.io/datumaro/docs/user-manual/supported_formats).
Other formats and documentation for them can be found [here](https://openvinotoolkit.github.io/datumaro/latest/docs/data-formats/supported_formats).
- Dataset building
- Merging multiple datasets into one
- Dataset filtering by a custom criteria:
Expand Down Expand Up @@ -85,10 +86,10 @@ CVAT annotations ---> Publication, statistics etc.
- RISE for object detection

> Check
[the design document](https://openvinotoolkit.github.io/datumaro/docs/design)
[the design document](https://openvinotoolkit.github.io/datumaro/latest/docs/explanation/architecture)
for a full list of features.
> Check
[the user manual](https://openvinotoolkit.github.io/datumaro/docs/user-manual)
[the user manual](https://openvinotoolkit.github.io/datumaro/latest/docs/user-manual/how_to_use_datumaro)
for usage instructions.

## Contributing
Expand All @@ -99,12 +100,12 @@ Feel free to
[open an Issue](https://github.com/openvinotoolkit/datumaro/issues/new), if you
think something needs to be changed. You are welcome to participate in
development, instructions are available in our
[contribution guide](https://openvinotoolkit.github.io/datumaro/docs/contributing).
[contribution guide](https://github.com/openvinotoolkit/datumaro/blob/develop/contributing.md).

## Telemetry data collection note

The [OpenVINO™ telemetry library](https://github.com/openvinotoolkit/telemetry/)
is used to collect basic information about Datumaro usage.

To enable/disable telemetry data collection please see the
[guide](https://openvinotoolkit.github.io/datumaro/docs/user-manual/how_to_control_tm_data_collection/).
[guide](https://openvinotoolkit.github.io/datumaro/latest/docs/user-manual/how_to_control_tm_data_collection).
2 changes: 1 addition & 1 deletion datumaro/cli/util/__init__.py
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Expand Up @@ -107,7 +107,7 @@ def show_video_import_warning():
"system setup changes (library version, OS, etc.). "
"If you need stable results, consider splitting the video "
"manually using instructions at: "
"https://openvinotoolkit.github.io/datumaro/docs/media_formats/"
"https://openvinotoolkit.github.io/datumaro/latest/docs/data-formats/media_formats"
)


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2 changes: 1 addition & 1 deletion docs/source/docs/get-started/introduction.rst
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Expand Up @@ -43,7 +43,7 @@ Datumaro supports the following features:
(`bboxes`, `masks`)
- `YOLO <https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data>`_ (`bboxes`)

Other formats and documentation for them can be found `here <https://openvinotoolkit.github.io/datumaro/docs/user-manual/supported_formats>`_.
Other formats and documentation for them can be found `here <https://openvinotoolkit.github.io/datumaro/latest/docs/data-formats/supported_formats>`_.
- Dataset building

- Merging multiple datasets into one
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2 changes: 1 addition & 1 deletion notebooks/06_tiling.ipynb
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Expand Up @@ -32,7 +32,7 @@
" ├── ...\n",
"```\n",
"\n",
"However, for import compatibility, `obj.names` file must be added to `d6-dice/obj.names` filepath for import compatibility. This `obj.names` file includes the label names of the dataset, e.g., `[dice1, ..., dice6]`. Therefore, you can write it with the following simple code. Please see [Yolo Loose format](https://openvinotoolkit.github.io/datumaro/docs/formats/yolo/) for more details."
"However, for import compatibility, `obj.names` file must be added to `d6-dice/obj.names` filepath for import compatibility. This `obj.names` file includes the label names of the dataset, e.g., `[dice1, ..., dice6]`. Therefore, you can write it with the following simple code. Please see [Yolo Loose format](https://openvinotoolkit.github.io/datumaro/latest/docs/explanation/formats/yolo) for more details."
]
},
{
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2 changes: 1 addition & 1 deletion notebooks/08_e2e_example_yolo_ultralytics_trainer.ipynb
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Expand Up @@ -23,7 +23,7 @@
" ├── ...\n",
"```\n",
"\n",
"However, for import compatibility, `obj.names` file must be added to `d6-dice/obj.names` filepath for import compatibility. This `obj.names` file includes the label names of the dataset, e.g., `[dice1, ..., dice6]`. Therefore, you can write it with the following simple code. Please see [Yolo Loose format](https://openvinotoolkit.github.io/datumaro/docs/formats/yolo/) for more details."
"However, for import compatibility, `obj.names` file must be added to `d6-dice/obj.names` filepath for import compatibility. This `obj.names` file includes the label names of the dataset, e.g., `[dice1, ..., dice6]`. Therefore, you can write it with the following simple code. Please see [Yolo Loose format](https://openvinotoolkit.github.io/datumaro/latest/docs/explanation/formats/yolo) for more details."
]
},
{
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2 changes: 1 addition & 1 deletion notebooks/09_encrypt_dataset.ipynb
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Expand Up @@ -23,7 +23,7 @@
" ├── ...\n",
"```\n",
"\n",
"However, for import compatibility, `obj.names` file must be added to `d6-dice/obj.names` filepath for import compatibility. This `obj.names` file includes the label names of the dataset, e.g., `[dice1, ..., dice6]`. Therefore, you can write it with the following simple code. Please see [Yolo Loose format](https://openvinotoolkit.github.io/datumaro/docs/formats/yolo/) for more details."
"However, for import compatibility, `obj.names` file must be added to `d6-dice/obj.names` filepath for import compatibility. This `obj.names` file includes the label names of the dataset, e.g., `[dice1, ..., dice6]`. Therefore, you can write it with the following simple code. Please see [Yolo Loose format](https://openvinotoolkit.github.io/datumaro/latest/docs/explanation/formats/yolo) for more details."
]
},
{
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2 changes: 1 addition & 1 deletion notebooks/10_noisy_label_detection.ipynb
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Expand Up @@ -213,7 +213,7 @@
"source": [
"## Import statistics and analyze them to find the noisy label samples\n",
"\n",
"In this step, we import the statistics obtained during the model training to find the noisy label samples. Because we set the output directory as `outputs` in the previous step, we you can find the dataset including statistics which is exported to [Datumaro format](https://openvinotoolkit.github.io/datumaro/docs/formats/datumaro/) at `outputs/logs/noisy_label_detection`. To make training loss dynamics analysis easier, we provide an interface called `dm.LossDynamicsAnalyzer`. You can get the top-k candidates for noisy label samples using it. Additionally, we can also plot the exponential moving average (EMA) training loss curves of top-k candidates and compare them with the average of samples in the dataset. The EMA training loss is a very strong criterion for noisy label samples. For more detail, please see [1].\n",
"In this step, we import the statistics obtained during the model training to find the noisy label samples. Because we set the output directory as `outputs` in the previous step, we you can find the dataset including statistics which is exported to [Datumaro format](https://openvinotoolkit.github.io/datumaro/latest/docs/explanation/formats/datumaro) at `outputs/logs/noisy_label_detection`. To make training loss dynamics analysis easier, we provide an interface called `dm.LossDynamicsAnalyzer`. You can get the top-k candidates for noisy label samples using it. Additionally, we can also plot the exponential moving average (EMA) training loss curves of top-k candidates and compare them with the average of samples in the dataset. The EMA training loss is a very strong criterion for noisy label samples. For more detail, please see [1].\n",
"\n",
"[1]: Zhou, Tianyi, Shengjie Wang, and Jeff Bilmes. \"Robust curriculum learning: from clean label detection to noisy label self-correction.\" International Conference on Learning Representations. 2021."
]
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2 changes: 1 addition & 1 deletion notebooks/11_validate.ipynb
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Expand Up @@ -7,7 +7,7 @@
"source": [
"# Validate datasets\n",
"\n",
"In this notebook example, we are going to generate the validation report, which contains types of anomaly data regarding to https://openvinotoolkit.github.io/datumaro/docs/user-manual/command-reference/validate. Through this, we can identify which data was inapplicable for DL workflow because it is broken or undefined. Moreover, we detect imbalanced, out-of-distributed, or rare samples together.\n",
"In this notebook example, we are going to generate the validation report, which contains types of anomaly data regarding to https://openvinotoolkit.github.io/datumaro/latest/docs/command-reference/context_free/validate. Through this, we can identify which data was inapplicable for DL workflow because it is broken or undefined. Moreover, we detect imbalanced, out-of-distributed, or rare samples together.\n",
"\n",
"## Prerequisite \n",
"### Download COCO 2017 validation dataset \n",
Expand Down

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