Scheduled weekly dependency update for week 48 by pyup-bot · Pull Request #556 · DagsHub/client

pyup-bot · 2024-12-02T14:53:10Z

Update sphinx from 7.3.7 to 8.1.3.

Changelog

8.1.3

==============================

Bugs fixed
----------

* 13013: Restore support for :func:`!cut_lines` with no object type.
Patch by Adam Turner.

8.1.2

=====================================

Bugs fixed
----------

* 13012: Expose :exc:`sphinx.errors.ExtensionError` in ``sphinx.util``
for backwards compatibility.
This will be removed in Sphinx 9, as exposing the exception
in ``sphinx.util`` was never intentional.
:exc:`!ExtensionError` has been part of ``sphinx.errors`` since Sphinx 0.9.
Patch by Adam Turner.

8.1.1

=====================================

Bugs fixed
----------

* 13006: Use the preferred https://www.cve.org/ URL for
the :rst:role:`:cve: &lt;cve&gt;` role.
Patch by Hugo van Kemenade.
* 13007: LaTeX: Improve resiliency when the required
``fontawesome`` or ``fontawesome5`` packages are not installed.
Patch by Jean-François B.

8.1.0

=====================================

Dependencies
------------

* 12756: Add lower-bounds to the ``sphinxcontrib-*`` dependencies.
Patch by Adam Turner.
* 12833: Update the LaTeX ``parskip`` package from 2001 to 2018.
Patch by Jean-François B.

Incompatible changes
--------------------

* 12763: Remove unused internal class ``sphinx.util.Tee``.
Patch by Adam Turner.
* 12822: LaTeX: for Unicode engines, the :ref:`fvset` default is changed to
``&#x27;\\fvset{fontsize=auto}&#x27;`` from ``&#x27;\\fvset{fontsize=\\small}&#x27;``.
Code-blocks are unchanged as FreeMono is now loaded with ``Scale=0.9``.
An adjustment to existing projects is needed only if they used a custom
:ref:`fontpkg` configuration and did not set :ref:`fvset`.
Patch by Jean-François B.
* 12875: Disable smartquotes for languages: ``zh_CN`` and ``zh_TW`` by default.
Patch by A. Rafey Khan.

Deprecated
----------

* 12762: Deprecate ``sphinx.util.import_object``.
Use :py:func:`importlib.import_module` instead.
Patch by Adam Turner.
* 12766: Deprecate ``sphinx.util.FilenameUniqDict``
and ``sphinx.util.DownloadFiles``.
Patch by Adam Turner.

Features added
--------------

* 11328: Mention evaluation of templated content during production of static
output files.
Patch by James Addison.
* 12704: LaTeX: make :dudir:`contents &lt;table-of-contents&gt;`, :dudir:`topic`,
and :dudir:`sidebar` directives separately customizable for PDF output.
Patch by Jean-François B. and Bénédikt Tran.
* 12474: Support type-dependent search result highlighting via CSS.
Patch by Tim Hoffmann.
* 12652: LaTeX: Add :confval:`math_numsep` support to latex builder.
Patch by Thomas Fanning and Jean-François B.
* 12743: No longer exit on the first warning when
:option:`--fail-on-warning &lt;sphinx-build --fail-on-warning&gt;` is used.
Instead, exit with a non-zero status if any warnings were generated
during the build.
Patch by Adam Turner.
* 12743: Add :option:`sphinx-build --exception-on-warning`,
to raise an exception when warnings are emitted during the build.
Patch by Adam Turner and Jeremy Maitin-Shepard.
* 12907: Add :confval:`html_last_updated_use_utc` to allow using
universal time (GMT/UTC) instead of local time for the date-time
supplied to :confval:`html_last_updated_fmt`.
Patch by Adam Turner.
* 12910: Copyright entries now support the ``&#x27;%Y&#x27;`` placeholder
to substitute the current year.
This is helpful for reducing the reliance on Python modules
such as :py:mod:`time` or :py:mod:`datetime` in :file:`conf.py`.
See :ref:`the docs &lt;config-copyright&gt;` for further detail.
Patch by Adam Turner.
* 11781: Add roles for referencing CVEs (:rst:role:`:cve: &lt;cve&gt;`)
and CWEs (:rst:role:`:cwe: &lt;cwe&gt;`).
Patch by Hugo van Kemenade.
* 11809: Improve the formatting for RFC section anchors.
Patch by Jakub Stasiak and Adam Turner.
* 12852: Support a :attr:`.Builder.supported_linkcode` attribute
for builders to enable use of :mod:`sphinx.ext.linkcode`-generated
references.
Patch by James Knight.
* 12949: Print configuration options that differ from the pickled environment.
This can be helpful in diagnosing the cause of a full rebuild.
Patch by Adam Turner.

Bugs fixed
----------

* 12514: intersphinx: fix the meaning of a negative value for
:confval:`intersphinx_cache_limit`.
Patch by Shengyu Zhang.
* 12722: LaTeX: avoid TeX reporting ``Overfull \hbox`` from too long
strings in a codeline when the problem has actually been solved thanks
to :ref:`latexsphinxsetupforcewraps`.
Patch by Jean-François B.
* 12730: The ``UnreferencedFootnotesDetector`` transform has been improved
to more consistently detect unreferenced footnotes.
Note, the priority of the transform has been changed from 200 to 622,
so that it now runs after the docutils ``Footnotes`` resolution transform.
Patch by Chris Sewell.
* 12778: LaTeX: let :ref:`&#x27;sphinxsetup&#x27; &lt;latexsphinxsetup&gt;`
``div.topic_box-shadow`` key if used with only one dimension set both
x-offset and y-offset as per documentation.
Patch by Jean-François B.
* 12587: Do not warn when potential ambiguity detected during Intersphinx
resolution occurs due to duplicate targets that differ case-insensitively.
Patch by James Addison.
* 12639: Fix singular and plural search results text.
Patch by Hugo van Kemenade.
* 12645: Correctly support custom gettext output templates.
Patch by Jeremy Bowman.
* 12717: LaTeX: let :option:`-q &lt;sphinx-build -q&gt;` (quiet) option for
:program:`sphinx-build -M latexpdf` or :program:`make latexpdf` (``O=-q``)
get passed to :program:`latexmk`.  Let :option:`-Q &lt;sphinx-build -Q&gt;`
(silent) apply as well to the PDF build phase.
Patch by Jean-François B.
* 12744: LaTeX: Classes injected by a custom interpreted text role now give
rise to nested ``\DUrole``&#x27;s, rather than a single one with comma separated
classes.
Patch by Jean-François B.
* 12831: LaTeX: avoid large voids sometimes occurring at page bottoms.
Patch by Jean-François B.
* 11970, 12551: singlehtml builder: make target URIs to be same-document
references in the sense of :rfc:`RFC 3986, §4.4 &lt;3986section-4.4&gt;`,
e.g., ``index.htmlfoo`` becomes ``foo``.
(note: continuation of a partial fix added in Sphinx 7.3.0)
Patch by James Addison (with reference to prior work by Eric Norige).
* 12735: Fix :pep:`695` generic classes LaTeX output formatting.
Patch by Jean-François B. and Bénédikt Tran.
* 12782: intersphinx: fix double forward slashes when generating the inventory
file URL (user-defined base URL of an intersphinx project are left untouched
even if they end with double forward slashes).
Patch by Bénédikt Tran.
* 12796: Enable parallel reading if requested,
even if there are fewer than 6 documents.
Patch by Matthias Geier.
* 12844: Restore support for ``:noindex:`` for the :rst:dir:`js:module`
and :rst:dir:`py:module` directives.
Patch by Stephen Finucane.
* 12916: Restore support for custom templates named with the legacy ``_t``
suffix during ``apidoc`` RST rendering (regression in 7.4.0).
Patch by James Addison.
* 12451: Only substitute copyright notice years with values from
``SOURCE_DATE_EPOCH`` for entries that match the current system clock year,
and disallow substitution of future years.
Patch by James Addison and Adam Turner.
* 12905: intersphinx: fix flipped use of :confval:`intersphinx_cache_limit`,
which always kept the cache for positive values, and always refreshed it for
negative ones.
Patch by Nico Madysa.
* 12888: Add a warning when document is included in multiple toctrees
and ensure deterministic resolution of global toctree in parallel builds
by choosing the lexicographically greatest parent document.
Patch by A. Rafey Khan
* 12995: Significantly improve performance when building the search index
for Chinese languages.
Patch by Adam Turner.
* 12767: :py:meth:`.Builder.write` is typed as ``final``, meaning that the
:event:`write-started` event may be relied upon by extensions.
A new :py:meth:`.Builder.write_documents` method has been added to
control how documents are written.
This is intended for builders that do not output a file for each document.
Patch by Adam Turner.


Testing
-------

* 12141: Migrate from the deprecated ``karma`` JavaScript test framework to
the actively-maintained ``jasmine`` framework.  Test coverage is unaffected.
Patch by James Addison.

Links

PyPI: https://pypi.org/project/sphinx
Changelog: https://data.safetycli.com/changelogs/sphinx/

Update furo from 2024.5.6 to 2024.8.6.

Changelog

2024.08.06

- ✨ Add support for Sphinx 8
- ✨ Add smoother transitions between breakpoints
- Increase specificity of table-wrapper selector
- Avoid page breaks inside paragraphs

2024.07.18

- Improve how icons are handled and aligned.
- Improve scroll event handler.
- Hide the copybutton by default.
- Fix `source_view_link` configuration handling.
- Fix close tag on pencil icon.

Links

PyPI: https://pypi.org/project/furo
Changelog: https://data.safetycli.com/changelogs/furo/

Update sphinx-autobuild from 2024.4.16 to 2024.10.3.

Changelog

2024.10.03

-----------------------

* Improve error handling for failures in pre-build commands.

2024.10.02

-----------------------

* Show the changed paths that triggered the rebuild.

2024.09.19

-----------------------

* Fix path filtering on Windows by normalising path separators.
* Filter various directories by default (``.git``, ``venv``, etc).
* Serve the correct directory when using make mode (``-M``).

2024.09.18

-----------------------

* Run Sphinx through the Python entry point rather than the binary on PATH.

2024.09.17

-----------------------

* Relax checks for paths that aren&#x27;t required to exist.

2024.09.03

-----------------------

* Fix support for Python 3.9.
* Fix running ``sphinx-autobuild`` via entry point scripts.
* Run ``sphinx-build`` in a subprocess to mitigate autdoc issues.
* Support the ``-M`` &#x27;make mode&#x27; option for ``sphinx-build``.

Links

PyPI: https://pypi.org/project/sphinx-autobuild
Changelog: https://data.safetycli.com/changelogs/sphinx-autobuild/

Update setuptools from 70.0.0 to 75.6.0.

The bot wasn't able to find a changelog for this release. Got an idea?

Links

PyPI: https://pypi.org/project/setuptools

Update pytest from 8.2.1 to 8.3.4.

The bot wasn't able to find a changelog for this release. Got an idea?

Links

PyPI: https://pypi.org/project/pytest

Update pytest-env from 1.1.3 to 1.1.5.

Changelog

1.1.5

&lt;!-- Release notes generated using configuration in .github/release.yml at main --&gt;

What&#x27;s Changed
* Improve the CI by gaborbernat in https://github.com/pytest-dev/pytest-env/pull/101


**Full Changelog**: https://github.com/pytest-dev/pytest-env/compare/1.1.4...1.1.5

1.1.4

&lt;!-- Release notes generated using configuration in .github/release.yml at main --&gt;



**Full Changelog**: https://github.com/pytest-dev/pytest-env/compare/1.1.3...1.1.4

Links

PyPI: https://pypi.org/project/pytest-env
Changelog: https://data.safetycli.com/changelogs/pytest-env/

Update fiftyone from 0.23.8 to 1.0.2.

Changelog

1.0.1

Please see the official [Voxel51 Release Notes](https://docs.voxel51.com/release-notes.html#fiftyone-1-0-1) for details!

1.0.0

Please see the official Voxel51 [Release Notes](https://docs.voxel51.com/release-notes.html#fiftyone-1-0-0)

0.25.2

Please see the official Voxel51 [Release Notes](https://docs.voxel51.com/release-notes.html#fiftyone-0-25-2)

0.25.1

Please see the official Voxel51 [Release Notes](https://docs.voxel51.com/release-notes.html#fiftyone-0-25-1)

0.25.0

Please see the official Voxel51 [Release Notes](https://docs.voxel51.com/release-notes.html#fiftyone-0-25-0)

0.24.1

Please see the official [Voxel51 Release Notes](https://docs.voxel51.com/release-notes.html#fiftyone-0-24-1) for details.

0.24.0

Please see the official [Voxel51 Release Notes](https://docs.voxel51.com/release-notes.html#fiftyone-0-24-0) for details.

Links

PyPI: https://pypi.org/project/fiftyone
Changelog: https://data.safetycli.com/changelogs/fiftyone/
Repo: https://github.com/voxel51/fiftyone

Update datasets from 2.19.1 to 3.1.0.

Changelog

3.1.0

Added

-   [API] `tfds.builder_cls(name)` to access a DatasetBuilder class by name
-   [API] `info.split[&#x27;train&#x27;].filenames` for access to the tf-record files.
-   [API] `tfds.core.add_data_dir` to register an additional data dir.
-   [Testing] Support for custom decoders in `tfds.testing.mock_data`.
-   [Documentation] Shows which datasets are only present in `tfds-nightly`.
-   [Documentation] Display images for supported datasets.

Changed

-   Rename `tfds.core.NamedSplit`, `tfds.core.SplitBase` -&gt; `tfds.Split`. Now
 `tfds.Split.TRAIN`,... are instance of `tfds.Split`.
-   Rename `interleave_parallel_reads` -&gt; `interleave_cycle_length` for
 `tfds.ReadConfig`.
-   Invert ds, ds_info argument orders for `tfds.show_examples`.

Deprecated

-   `tfds.features.text` encoding API. Please use `tensorflow_text` instead.

Removed

-   `num_shards` argument from `tfds.core.SplitGenerator`. This argument was
 ignored as shards are automatically computed.
-   Most `ds.with_options` which where applied by TFDS. Now use `tf.data`
 default.

Fixed

-   Better error messages.
-   Windows compatibility.

3.0.0

Added

-   `DownloadManager` is now pickable (can be used inside Beam pipelines).
-   `tfds.features.Audio`:
 -   Support float as returned value.
 -   Expose sample_rate through `info.features[&#x27;audio&#x27;].sample_rate`.
 -   Support for encoding audio features from file objects.
-   More datasets.

Changed

-   New `image_classification` section. Some datasets have been move there from
 `images`.
-   `DownloadConfig` does not append the dataset name anymore (manual data
 should be in `&lt;manual_dir&gt;/` instead of `&lt;manual_dir&gt;/&lt;dataset_name&gt;/`).
-   Tests now check that all `dl_manager.download` urls has registered
 checksums. To opt-out, add `SKIP_CHECKSUMS = True` to your
 `DatasetBuilderTestCase`.
-   `tfds.load` now always returns `tf.compat.v2.Dataset`. If you&#x27;re using still
 using `tf.compat.v1`:
 -   Use `tf.compat.v1.data.make_one_shot_iterator(ds)` rather than
     `ds.make_one_shot_iterator()`.
 -   Use `isinstance(ds, tf.compat.v2.Dataset)` instead of `isinstance(ds,
     tf.data.Dataset)`.

Deprecated

-   The `tfds.features.text` encoding API is deprecated. Please use
 [tensorflow_text](https://www.tensorflow.org/tutorials/tensorflow_text/intro)
 instead.
-   `num_shards` argument of `tfds.core.SplitGenerator` is currently ignored and
 will be removed in the next version.

Removed

-   Legacy mode `tfds.experiment.S3` has been removed
-   `in_memory` argument has been removed from `as_dataset`/`tfds.load` (small
 datasets are now auto-cached).
-   `tfds.Split.ALL`.

Fixed

-   Various bugs, better error messages, documentation improvements.

Links

PyPI: https://pypi.org/project/datasets
Changelog: https://data.safetycli.com/changelogs/datasets/
Repo: https://github.com/huggingface/datasets/tags

Update ultralytics from 8.2.48 to 8.3.39.

Changelog

8.3.39

🌟 Summary  
The Ultralytics `v8.3.39` release focuses on improving model behavior, functionality, and user experience across multiple aspects, including classification validation, documentation enhancements, and tool usability. It introduces critical fixes and new features to improve the overall quality of the platform. 🚀

---

📊 Key Changes  

- 🧠 **Fixed Classification Validation Loss:**
- Adjusted classification model&#x27;s loss scaling during validation to improve output consistency and accuracy.
- Introduced a refined approach to apply `softmax` only in necessary scenarios for clarity and precision.

- 🎯 **&quot;Classes&quot; Filter in Training:**
- Added a new `classes` argument to the training configuration, enabling model training on specific class IDs selectively.  

- 🎥 **Enhanced Video Annotation Tool:**
- Introduced a &quot;Sweep Annotation&quot; utility for dynamic video annotation. Users can now visualize objects based on an interactive sweep line that tracks their positions.  

- 🎨 **Improved Color Handling in LibTorch Example**:
- Addressed a key issue by adding a **BGR to RGB conversion** step in the C++ LibTorch inference example, ensuring color compatibility for accurate YOLO results.

- 🗂️ **Documentation Updates:**
- Significant improvements in README files:
 - Clickable YOLO11 performance plot images now redirect to documentation.
 - Enhanced clarity about model auto-download behavior and training details.
- Added new high-quality tutorial videos across docs for better onboarding and understanding.
- Fixed `YOLOv11` references to the correct term `YOLO11` for consistency.

- ⚙️ **Code Improvements and Maintenance:**
- Simplified segmentation handling with better clipping (`clip()`) for out-of-bounds coordinates in segmentation tasks.
- Added an elegant `__getattr__` method making model attributes (e.g., `stride` or `task`) directly accessible from the `Model` class.
- Refined model logging for better debugging and developer experience.

---

🎯 Purpose &amp; Impact  

- **Enhanced Accuracy and Model Behavior**: The classification loss scaling fix addresses a crucial inconsistency, delivering more reliable results during validation phases.
- **Increased Flexibility**: The &quot;classes&quot; argument empowers users with precise control, making training workflows more tailored and efficient by focusing on specific class IDs. 💡
- **Better Video Annotation**: The &quot;Sweep Annotation&quot; tool adds an intuitive way to annotate video data interactively, offering new possibilities for detection and tracking tasks.
- **Improved Inference Quality**: The BGR to RGB fix ensures accurate detections for users operating in C++ environments with LibTorch inference.
- **Streamlined User Education**: Updated and accessible documentation alongside engaging video tutorials helps onboard new users quickly while enhancing knowledge for experienced developers. 📚
- **Consistency**: Terminology such as `YOLO11` aligned across documentation ensures clarity and avoids user confusion.

This release keeps refining both functionality and usability, advancing the YOLO ecosystem for a diverse range of practical applications. 🎉

What&#x27;s Changed
* Add YOLO11 docs page redirect in `README.md` and `README.zh-CN.md` by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17806
* Fix missing labels when all segment points are out of bounds by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17810
* New Solutions sweep counting annotator by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17742
* Improved Docs minify by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17816
* Revert Docs minify attempt by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17831
* Update format.yml Discord and Kaggle links by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17814
* Update contributing.md with open-sourcing guide by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17832
* Fix YOLO11 usage by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17834
* Update Tasks Banner in README.md by pderrenger in https://github.com/ultralytics/ultralytics/pull/17833
* Fix region-counting indents by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17835
* Update Tasks banner spacing by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17843
* Add functions descriptions in `plotting.py` by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17813
* Remove Docs Tasks banner linebreak by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17844
* Add https://youtu.be/-aYO-6VaDrw and https://youtu.be/M7xWw4Iodhg to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17863
* Add `classes` to train arguments by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17856
* Add BGR to RGB conversion in LibTorch example by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17864
* `__getattr__` support to access YOLO attributes via Model class by WYYAHYT in https://github.com/ultralytics/ultralytics/pull/17805
* `ultralytics 8.3.39` fix classification validation loss scaling by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17851

New Contributors
* WYYAHYT made their first contribution in https://github.com/ultralytics/ultralytics/pull/17805

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.38...v8.3.39

8.3.38

🌟 Summary
The release of &#x27;v8.3.38&#x27; introduces significant enhancements, particularly emphasizing video interaction capabilities through the new `SAM2VideoPredictor` class for object segmentation and tracking in videos. This update also includes general improvements and optimizations across various modules.

📊 Key Changes
- **SAM2VideoPredictor**: A new class aimed at enhancing video segmentation and object tracking, supporting advanced interactions such as prompts for segment modifications.
- **Improved Video Segmentation**: Features non-overlapping masks, better memory management, and support for interactive user prompts.
- **Configuration Clean-Up**: Removal of obsolete parameters such as `label_smoothing`.
- **Platform Compatibility**: Extended detection for NVIDIA Jetson devices, accommodating more models.
- **Documentation and Code Updates**: Adjustments for improved clarity and accuracy in both code and documentation.

🎯 Purpose &amp; Impact
- 📽️ **Enhanced Video Interaction**: The `SAM2VideoPredictor` allows users to fine-tune video processing outputs dynamically, making video segmentation more precise and interactive.
- 🚀 **Efficiency &amp; Resource Management**: Optimized memory use during video segmentation leads to faster inferencing and resource savings, beneficial for running on resource-constrained devices.
- 🛠️ **Code Simplification**: Removing unnecessary parameters like `label_smoothing` helps streamline configuration settings, reducing potential user confusion.
- 📱 **Broader Device Support**: Updating device compatibility ensures the software is functional across a wider range of hardware, improving the user experience for those utilizing NVIDIA Jetson platforms.
- 📚 **Improved User Documentation**: Enhanced documentation aids both beginners and advanced users by making it easier to understand and implement model configurations and changes efficiently.

What&#x27;s Changed
* Delete .github/workflows/codeql.yaml by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17772
* Fix `RepC3` module for RT-DETR models by Andrewymd in https://github.com/ultralytics/ultralytics/pull/17086
* Removes unused argument  `label_smoothing` by Burhan-Q in https://github.com/ultralytics/ultralytics/pull/16014
* Concat all segments by default for multi-part masks by Y-T-G in https://github.com/ultralytics/ultralytics/pull/16826
* Improve `is_jetson` to support more Jetson devices by lakshanthad in https://github.com/ultralytics/ultralytics/pull/17770
* Fix DLA export by Laughing-q in https://github.com/ultralytics/ultralytics/pull/17765
* Fix CI.md CodeQL badges by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17779
* Fix Prettier docs issues by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17798
* Convert frames to RGB before SAHI inference by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17795
* Fix parking points annotator for macOS by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17797
* Fix `forward_fuse` comment by arun477 in https://github.com/ultralytics/ultralytics/pull/17714
* `ultralytics 8.3.38` SAM 2 video inference by Laughing-q in https://github.com/ultralytics/ultralytics/pull/14851

New Contributors
* Andrewymd made their first contribution in https://github.com/ultralytics/ultralytics/pull/17086
* arun477 made their first contribution in https://github.com/ultralytics/ultralytics/pull/17714

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.37...v8.3.38

8.3.37

🌟 Summary
The release of `v8.3.37` introduces significant improvements and fixes across the export functionality and model operation modes, aiming to streamline user experience and enhance performance when using Ultralytics tools. 

📊 Key Changes
- **TensorRT Auto-Workspace Size**: Implements an auto-managed workspace size for TensorRT exports by default, allowing more flexibility and reducing manual configuration errors.
- **Label Padding Adjustment**: Optimized the label augmentation by correctly updating vertical and horizontal padding, enhancing image annotation accuracy.
- **Model Evaluation Mode**: Introduced an `eval` method to easily switch models between training and evaluation modes, ensuring consistent performance during model assessments.
- **Documentation Updates**: Added video tutorials for better understanding of hand keypoint estimation and annotation utilities, and standardized dataset configuration references for clarity.

🎯 Purpose &amp; Impact
- **Ease of Use**: Setting the TensorRT `workspace` to `None` by default takes the burden off users to configure export parameters manually, simplifying the model export process.
- **Improved Accuracy**: The fix in label padding ensures accurate annotations, critical for reliable model training and evaluation.
- **Consistent Evaluation**: By allowing models to switch to evaluation mode seamlessly, users will experience more reliable model performance metrics which are crucial for assessments.
- **Enhanced Learning Resources**: With new video tutorials, users can gain a deeper understanding of utilizing Ultralytics features, potentially increasing the adoption and correct usage of functionalities.
- **Documentation Consistency**: Transitioning to a uniform dataset configuration in examples reduces confusion, making it easier for users to follow guides and setups.

What&#x27;s Changed
* Fix labels padding for Letterbox with `center=False` by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17728
* Add https://youtu.be/c-S5M36XWmg &amp; https://youtu.be/fd6u1TW_AGY to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17722
* Update `coco-seg.yaml` to `coco.yaml` by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17739
* Bump astral-sh/setup-uv from 3 to 4 in /.github/workflows by dependabot[bot] in https://github.com/ultralytics/ultralytics/pull/17753
* Standardize default region points in docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17721
* Enable model.eval() usage for `YOLO` class by Laughing-q in https://github.com/ultralytics/ultralytics/pull/17754
* `ultralytics 8.3.37` TensorRT auto-workspace size by Burhan-Q in https://github.com/ultralytics/ultralytics/pull/17748


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.36...v8.3.37

8.3.36

🌟 Summary
This release focuses on enhancing compatibility with OpenVINO, refining documentation, optimizing code performance, and improving theming logic in documentation.

📊 Key Changes
- **OpenVINO Compatibility:** Updated the Ultralytics package to version 8.3.36; OpenVINO and NNCF dependencies now require newer versions.
- **Documentation Tweaks:** Corrected model names and improved documentation consistency in export tables.
- **Code Refactoring:** Streamlined and optimized JavaScript and Python code to enhance readability, maintainability, and performance.
- **Theme Management:** Refined theme change logic in documentation, improving the user experience when switching between light and dark modes.
- **Region Points Update:** Standardized default region points for more accurate object counting tasks.

🎯 Purpose &amp; Impact
- **Enhanced Tool Compatibility:** Ensures the software works smoothly with the latest OpenVINO version, especially on macOS, reducing export issues. 🖥️
- **Improved Documentation Accuracy:** Accurate model references and improved readability prevent user confusion. 📚
- **Efficiency and Performance:** Optimized code results in faster execution which enhances productivity and user experience. 🚀
- **Better User Experience:** Improved theme logic offers a smoother transition between modes, enhancing the user interface interaction. 🌗
- **Reliable Object Detection:** Revising region points leads to more consistent and reliable object detection and tracking outcomes. 📐🔍

What&#x27;s Changed
* Fix `imx500` YOLO support in export table by lakshanthad in https://github.com/ultralytics/ultralytics/pull/17702
* Ultralytics Refactor https://ultralytics.com/actions by pderrenger in https://github.com/ultralytics/ultralytics/pull/17701
* Update extra.js by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17705
* Minify-html fix by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17706
* `extra.js` dark mode fix by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17707
* Benchmarks graph Javascript fix by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17700
* Standardize default region points by Jerry-Kon in https://github.com/ultralytics/ultralytics/pull/17715
* `ultralytics 8.3.36` unpin OpenVINO ARM install version by adrianboguszewski in https://github.com/ultralytics/ultralytics/pull/16600

New Contributors
* Jerry-Kon made their first contribution in https://github.com/ultralytics/ultralytics/pull/17715

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.35...v8.3.36

8.3.35

🌟 Summary
This release, version 8.3.35, introduces enhanced support for models with dynamic shapes in image processing, making model handling more adaptable and efficient. 🚀

📊 Key Changes
- **Dynamic Models Support**: Improved the `pre_transform` function to enable automatic letterboxing when working with models that support dynamic input shapes.
- **Updated Docker Configuration**: Switched Docker&#x27;s base image to Python 3.11.10 for better consistency and added PaddlePaddle installation for broader compatibility.
- **Documentation Enhancements**: Improved Ray Tune documentation, benchmarking tools, and documentation site usability with a scalable search bar.
- **Cosmetic and Code Maintenance**: Various JavaScript updates for cleaner code structure and updated styles for improved user interaction.

🎯 Purpose &amp; Impact
- **Enhanced Model Handling**: By supporting dynamic shapes, the update ensures that users working with such models benefit from accurate image preprocessing and potentially improved performance.
- **Consistency and Compatibility**: Docker updates aid in consistent environment setup and extend functionality by supporting PaddlePaddle installations.
- **Improved User Experience**: Revised documentation and a smoother search experience make it easier for users to find information and ensure a seamless interaction with the site.
- **Developer-Focused Improvements**: Code and workflow updates facilitate easier maintenance and readability, enabling developers to work more efficiently.

What&#x27;s Changed
* Add RTDETRv2 in `benchmarks.md` chart 📈 by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17635
* Update extra.js by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17665
* Update Dockerfile-cpu to `ubuntu:latest` by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17670
* Docs Search Bar improvements by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17669
* Add missing import to Raytune docs by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17683
* `ultralytics 8.3.35` enable `auto` letterbox if model is `dynamic` by Laughing-q in https://github.com/ultralytics/ultralytics/pull/17687


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.34...v8.3.35

8.3.34

🌟 Summary
The update to version 8.3.34 focuses on improving prediction reliability in the FastSAM model and enhances various internal systems to optimize workflows and accuracy. 🚀

📊 Key Changes
- 🛠️ Enhanced FastSAM model&#x27;s `prompt` method to handle cases with empty predictions effectively.
- 🔧 Updated GitHub Actions to use `uv` for dependency installation, reducing potential Python packaging issues.
- 📋 Improved project name handling in training setups to fix issues with special characters, ensuring compatibility with systems like W&amp;B.
- 🔄 Revised `v8_transforms` function with better hyperparameter handling using `Namespace`.
- 🚀 Enhanced dataset configuration for RT-DETR with new parameters like `fraction`, `single_cls`, and `classes` to better align with YOLO dataset management.
- 📈 Refined object counting method in heatmaps to use centroids instead of bounding boxes for improved accuracy.

🎯 Purpose &amp; Impact
- ✅ **Reliable Predictions**: The FastSAM model update helps avoid errors during inference when some results are empty, making the prediction process more robust.
- 💡 **Streamlined Workflows**: Switching to `uv` in GitHub Actions enhances dependency management and ensures smoother continuous integration.
- 🗄️ **Project Naming Flexibility**: By reformatting project names, users will face fewer naming issues, particularly when integrating with various external systems.
- 📊 **Improved Handling of Hyperparameters**: Developers benefit from more manageable code and potentially fewer bugs with the new `Namespace` implementation.
- 🎯 **Enhanced Customization**: The dataset improvements allow users more control over the training process, focusing on specific classes and data subsets for faster experiments.
- 👁️‍🗨️ **Better Object Tracking**: The refined object counting mechanism boosts the precision of tracking, enhancing analytics accuracy which can significantly improve object detection applications.

What&#x27;s Changed
* Update Actions with uv installs by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17620
* Fix v8_transforms docstring example by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17630
* Fix W&amp;B project name separator compatibility by ArcPen in https://github.com/ultralytics/ultralytics/pull/17627
* Update Slack usage to v2 by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17631
* Add `fraction`, `single_cls` and `classes` to `RTDETRDataset` by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17633
* Heatmaps bug fix by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17634
* `ultralytics 8.3.34` FastSAM non-detection fix by petercham in https://github.com/ultralytics/ultralytics/pull/17628

New Contributors
* ArcPen made their first contribution in https://github.com/ultralytics/ultralytics/pull/17627
* petercham made their first contribution in https://github.com/ultralytics/ultralytics/pull/17628

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.33...v8.3.34

8.3.33

🌟 Summary
The latest release, v8.3.33, primarily focuses on refining object counting in the Ultralytics YOLO framework, boosting accuracy for tracking objects across specified regions.

📊 Key Changes
- **Object Counting Enhancement**: Overhauled the object counting logic by focusing on centroids for more precise tracking, especially in complex shapes and motions.
- **Updated Documentation**: Clarified the `retina_masks` and `device` arguments in the documentation for better user comprehension.
- **Expanded Hardware Compatibility**: Enabled MNN export on Raspberry Pi and NVIDIA Jetson platforms.
- **CI/CD Improvements**: Upgraded GitHub workflow actions for better integration with Codecov and Slack.

🎯 Purpose &amp; Impact
- **Improved Counting Accuracy**: By utilizing centroids over bounding boxes, the update ensures more reliable object tracking and counting, crucial for applications needing high precision. 🎯
- **User Clarity**: Enhanced documentation provides clearer guidelines, helping both novice and expert users understand configuration impacts better.
- **Broader Device Support**: Allowing MNN exports on more devices fosters flexibility and innovation, broadening the community&#x27;s ability to deploy models on diverse hardware setups.
- **Streamlined Workflows**: Upgrades to GitHub actions contribute to more efficient development cycles and error handling, ensuring smoother operations and faster updates.

What&#x27;s Changed
* Update `retina_masks` description by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17587
* Enable MNN on RPi and Jetson by lakshanthad in https://github.com/ultralytics/ultralytics/pull/17583
* Bump codecov/codecov-action from 4 to 5 in /.github/workflows by dependabot[bot] in https://github.com/ultralytics/ultralytics/pull/17597
* Bump slackapi/slack-github-action from 1.27.0 to 2.0.0 in /.github/workflows by dependabot[bot] in https://github.com/ultralytics/ultralytics/pull/17596
* Update `device` argument description for benchmark by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17550
* `ultralytics 8.3.33` Solutions counter direction fix by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17607


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.32...v8.3.33

8.3.32

🌟 Summary
The release of `v8.3.32` introduces a major new dataset called &quot;Dog-pose&quot;, designed for pose estimation tasks, along with some important improvements and fixes.

📊 Key Changes
- **Dog-pose Dataset**: Added a new dataset consisting of approximately 6,000 images with detailed annotations for 24 keypoints per dog, specifically for pose estimation using YOLO11.
- **Documentation Update**: Enhanced guides and introductory materials for the Dog-pose dataset, including usage through Python and CLI examples.
- **Link Fix**: Corrected a broken URL in the Jetson device setup documentation.
- **Workflow Update**: Extended retry delay for link checks in the GitHub workflow to enhance reliability.
- **Efficiency Fix**: Improved conditional logging for WandB reporting by checking the availability of plot data.

🎯 Purpose &amp; Impact
- 🐕 **Enhanced Pose Estimation**: The Dog-pose dataset greatly expands capabilities in animal pose estimation, useful in fields like veterinary research and animal behavior analysis.
- 📘 **User-Guidance**: Updated documentation makes it easier for users to leverage the new dataset effectively in their projects.
- 🔧 **Improved Accessibility**: Fixing documentation links enhances user experience by providing direct access to the correct setup resources.
- 🕒 **Optimized Workflow**: Longer delays between retries in automated link checks reduce server loads and improve the reliability of workflows.
- 🎨 **Efficient Resource Use**: The logging enhancement prevents the saving of unnecessary plots, optimizing storage and improving artifact management in model training.

What&#x27;s Changed
* Fix broken Jetson Doc URL by lakshanthad in https://github.com/ultralytics/ultralytics/pull/17549
* Update links.yml to 900s delay by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17576
* Fix: wandb reporting error if no positive examples by Jamil in https://github.com/ultralytics/ultralytics/pull/17544
* `ultralytics 8.3.32` New Dog-Pose dataset by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17556

New Contributors
* Jamil made their first contribution in https://github.com/ultralytics/ultralytics/pull/17544

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.31...v8.3.32

8.3.31

🌟 Summary
The `v8.3.31` release of Ultralytics introduces enhancements to automatic batch size estimation during model training, which aims to optimize memory usage and manage CUDA memory issues more effectively.

📊 Key Changes
- **Batch Size Optimization**: Implemented `auto_batch` functionality to determine the best batch size by evaluating memory consumption.
- **Improved Profiling**: The profiling tools have been updated to include a `max_num_obj` parameter for better batch size accuracy.
- **Error Management**: Introduced logging for CUDA out-of-memory warnings and an automatic switch to CPU computation when necessary.
- **Documentation Updates**: Removed the `verbose` argument from training documentation as it was deemed ineffective.

🎯 Purpose &amp; Impact
- **Efficient Memory Use**: Automatically adjusting batch sizes helps prevent overloading GPU memory, resulting in more efficient and stable training sessions. This is particularly beneficial for preventing abrupt interruptions due to memory errors.
- **Greater Reliability**: By switching to CPU processing when encountering memory errors, the system maintains training continuity, avoiding crashes and ensuring an uninterrupted user experience.
- **Simplified User Experience**: Streamlining training configuration by removing unnecessary options enhances usability, making the setup less complex for users.

What&#x27;s Changed
* Remove `verbose` arg from train docs. by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17257
* `ultralytics 8.3.31` add `max_num_obj` factor for `AutoBatch` by Laughing-q in https://github.com/ultralytics/ultralytics/pull/17514


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.30...v8.3.31

8.3.30

📊 Key Changes
- **Memory Management**: Implemented a CPU fallback mechanism for task alignment calculations if a GPU `torch.OutOfMemoryError` occurs.
- **Method Refactoring**: Introduced a helper method `_forward` to elegantly manage memory overflow conditions.
- **Docker and Documentation Fixes**: Improved Docker image tagging and fixed a broken Jetson device documentation link.
- **Enhanced Features**: Simplified documentation examples and introduced a new `RegionCounter` module for easier region-based object counting.

🎯 Purpose &amp; Impact
- **Stability and Reliability**: By ensuring task alignment processes can run on CPU under memory pressure, the update prevents application crashes and allows users with limited GPU resources to continue operations smoothly.
- **User Experience**: The changes make YOLO operations more flexible and robust, especially in environments with constrained computational resources, helping users to maintain performance without interruptions.
- **Documentation and Usability**: Improved documentation clarity makes it easier for both new and existing users to implement video analytics and other YOLO model features effectively. The `RegionCounter` addition simplifies integrating real-time object counting in specific video regions, broadening the tool&#x27;s practical applications. 🔧

These updates and enhancements ensure that users have a smoother and more reliable experience with Ultralytics YOLO, particularly in resource-constrained settings.

What&#x27;s Changed
* Fix Docker `jupyter` image naming by ambitious-octopus in https://github.com/ultralytics/ultralytics/pull/17479
* Include FPs for images with no labels in confusion matrix by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17481
* Simplify Solutions Docs code examples by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17493
* Fix broken Jetson doc URL by lakshanthad in https://github.com/ultralytics/ultralytics/pull/17519
* Update models.md by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17525
* Add return check for &#x27;yolo solutions help&#x27; by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17518
* Update sony-imx500.md by ServiAmirPM in https://github.com/ultralytics/ultralytics/pull/17491
* Fix `file_name` in `save_crop` by M3nxudo in https://github.com/ultralytics/ultralytics/pull/17499
* Add region counter as ultralytics solution by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17439
* `ultralytics 8.3.30` run TAL on CPU if `torch.OutOfMemoryError` by Laughing-q in https://github.com/ultralytics/ultralytics/pull/17515

New Contributors
* ServiAmirPM made their first contribution in https://github.com/ultralytics/ultralytics/pull/17491
* M3nxudo made their first contribution in https://github.com/ultralytics/ultralytics/pull/17499

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.29...v8.3.30

8.3.29

🌟 Summary
The v8.3.29 release has introduced a new capability in the Ultralytics YOLO framework, enabling the export of YOLOv8 models to the Sony IMX500 format. This advancement supports AI deployment on devices like Raspberry Pi AI Cameras, enhancing their utility for smart applications.

📊 Key Changes
- **Sony IMX500 Export Support**: Added the option to export models in the Sony IMX500 format, crucial for devices with constrained resources.
- **New `FXModel` Class**: Implemented for increased compatibility with torch.fx, facilitating advanced model manipulations.
- **Updated `.gitignore`**: Now ignores `*_imx_model/` directories, which store exported model artifacts.
- **Documentation and Tests**: Comprehensive documentation and tests added to cover the new export functionality, ensuring smooth user experience and reliability.

🎯 Purpose &amp; Impact
- **Enhanced Device Integration**: The ability to export to Sony&#x27;s IMX500 format allows for efficient AI processing on Raspberry Pi AI Cameras, making edge computing more viable and accessible. 🛠️🎥
- **Improved User Guidance**: The updated documentation provides clear steps for users to leverage this new feature, enabling developers to quickly integrate it into their projects. 📚
- **Streamlined Development Process**: The addition of the `FXModel` class and the support for the IMX500 format simplifies the model deployment process, further reducing the barriers to implementation on edge devices. 🖥️💡

What&#x27;s Changed
* Docs and CI updates by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17386
* Fix `model.end2end` assert by Laughing-q in https://github.com/ultralytics/ultralytics/pull/17391
* Add environment to publish.yml by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17395
* Fix PyPI downloads links by pderrenger in https://github.com/ultralytics/ultralytics/pull/17399
* Jupyter Docker Image, allow connection  by ambitious-octopus in https://github.com/ultralytics/ultralytics/pull/17400
* Fix duplicated annotations by `yolo_bbox2segment` by Laughing-q in https://github.com/ultralytics/ultralytics/pull/17401
* Add device selection for `yolo_bbox2segment` by Laughing-q in https://github.com/ultralytics/ultralytics/pull/17409
* Update validation-args.md by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17411
* Use new Ultralytics Retry action by pderrenger in https://github.com/ultralytics/ultralytics/pull/17419
* Changed model.names return type hint from list to dict[int, str] by KiSchnelle in https://github.com/ultralytics/ultralytics/pull/17402
* Match Docs caption to video thumbnail text by Burhan-Q in https://github.com/ultralytics/ultralytics/pull/17424
* Fix `conf` overwrite in results.py by keeper-jie in https://github.com/ultralytics/ultralytics/pull/17384
* Fix Ray Tune error by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17416
* Create sony-imx500.md standalone Docs page by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17452
* Update bug-report-template.yml to prompt for `collect_system_info` by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17466
* `ultralytics 8.3.29` Sony IMX500 export by Laughing-q in https://github.com/ultralytics/ultralytics/pull/14878

New Contributors
* keeper-jie made their first contribution in https://github.com/ultralytics/ultralytics/pull/17384
* KiSchnelle made their first contribution in https://github.com/ultralytics/ultralytics/pull/17402

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.28...v8.3.29

8.3.28

🌟 Summary
The release of version 8.3.28 introduces new command-line interface (CLI) commands for &quot;Solutions,&quot; allowing users to easily execute various video analytics tasks.

📊 Key Changes
- **New Solutions CLI Commands**: Users can now use CLI commands to apply different video analytics solutions without needing to modify arguments manually.
- **Additional CLI Examples**: Includes CLI examples for tasks like object counting, heatmaps, queue management, workout monitoring, speed estimation, and more, complete with customizable parameters.
- **Enhanced Auto-Annotation**: Improved auto-annotation functionality with new parameters like `max_det` to limit detections and `classes` for class-specific filtering.
- **Updated Documentation and Badges**: Enhancements in documentation accuracy with updated contributor details and added visibility via new badges in README files.
- **Rust and TFLite Examples**: New and improved examples for Rust ONNX runtime and TFLite Python integration for YOLO models.
- **New Docker Support**: Added a JupyterLab Docker image for improved interactive development support.

🎯 Purpose &amp; Impact
- **Ease of Use**: Simplifies using video analytics solutions directly from the command line, making it more accessible for users to implement complex video tasks with YOLO models.
- **Enhanced Control**: Users gain more precise control over dataset annotation outputs, aiding in task-specific preparation.
- **Improved Documentation**: Allows for better tracking of project metrics and user interactions with enhanced visibility.
- **Robust Cross-Platform Support**: New examples and JupyterLab Docker integration support diverse environments, improving user experience and accessibility.
- **Platform Precision**: Export compatibility checks ensure smoother model conversions across different hardware setups.

Overall, this release significantly enhances usability and equips users with flexible tools for effective computer vision tasks.

What&#x27;s Changed
* Fix `Bboxes` numpy.reshape by Laughing-q in https://github.com/ultralytics/ultralytics/pull/17301
* Fix MNN Raspberry Pi benchmark attempt by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17308
* Fix mkdocs_github_authors.yaml by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17314
* Update mkdocs_github_authors.yaml by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17320
* Refactor TFLite example. Support FP32, Fp16, INT8 models by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17317
* [Example] YOLO-Series(v5-11) ONNXRuntime Rust by jamjamjon in https://github.com/ultralytics/ultralytics/pull/17311
* Fix Docker badges by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17321
* Add ultralytics models publication notice in citations section by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17318
* Optimize Auto-Annotation with all args by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17315
* New JupyterLab Dockerfile by ambitious-octopus in https://github.com/ultralytics/ultralytics/pull/17071
* Update `overlap_mask` description. by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17324
* Generalized M1/M2 references to &quot;Apple silicon&quot; in train.md for broader inclusion by JairajJangle in https://github.com/ultralytics/ultralytics/pull/17330
* Add Albumentations Integrations Docs Page by abirami-vina in https://github.com/ultralytics/ultralytics/pull/17297
* Fix error on TensorRT export with float `workspace` value by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17352
* Added Error for TFLite int8 end2end model export  by ambitious-octopus in https://github.com/ultralytics/ultralytics/pull/17360
* Update kfold-cross-validation.md by M-Amrollahi in https://github.com/ultralytics/ultralytics/pull/17332
* `ultralytics 8.3.28` new Solutions CLI commands by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17233

New Contributors
* JairajJangle made their first contribution in https://github.com/ultralytics/ultralytics/pull/17330
* M-Amrollahi made their first contribution in https://github.com/ultralytics/ultralytics/pull/17332

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.27...v8.3.28

8.3.27

🌟 Summary
The `v8.3.27` release for Ultralytics primarily includes improvements in repository management and default training settings, alongside several enhancements to benchmarking and export functionality.

📊 Key Changes
- **Default Training Epochs**: Set a fallback of 100 epochs in `trainer.py` if no specific value is provided.
- **Author Information Update**: Refreshed GitHub avatars and usernames for contributors in documentation.
- **Removal of Jupyter Checks**: Cleaned up unnecessary checks for Jupyter notebooks in `checks.py`.
- **Benchmark Visualization**: Introduced interactive benchmark graphs in `benchmark.md` allowing dynamic model comparison through selectable checkboxes.
- **Export Compatibility**: Added checks to skip MNN export tests on Raspberry Pi and NVIDIA Jetson due to known unsupported issues.

🎯 Purpose &amp; Impact
- 🔧 **Enhanced Training Robustness**: By setting a default epoch, training processes are safeguarded against accidental misconfiguration, ensuring a more reliable setup for users.
- 🏷️ **Better Attribution**: Updating author profiles ensures that contributors receive the correct recognition, improving engagement and transparency in the community.
- 🛠️ **Clean Codebase**: Removing obsolete Jupyter checks streamlines the code, making the library more efficient and easier to maintain.
- 📊 **User-Friendly Benchmarking**: The addition of visual tools for model comparison makes it easier for users to evaluate performance metrics, aiding in informed decision-making.
- 🛡️ **Compatibility Safeguards**: Preventing exports to MNN format on unsupported devices avoids potential errors and enhances user experience by providing clear usage boundaries.

What&#x27;s Changed
* Add model comparison graphs in `benchmark.md` by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17212
* Skip MNN export for Raspberry Pi and NVIDIA Jetson by lakshanthad in https://github.com/ultralytics/ultralytics/pull/17292
* Benchmark graph fix by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17296
* `ultralytics 8.3.27` HUB timed training fix by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17298


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.26...v8.3.27

8.3.26

🌟 Summary
The release of Ultralytics version 8.3.26 focuses on enhancing performance for pose estimation tasks, improving export functionality for various formats, and incorporating user-centric updates for ease of use.

📊 Key Changes
- **Pose Task Enhancements:** Improved scaling for pose coordinates to boost accuracy in pose estimation tasks.
- **Export Improvements:** Enhanced export support, including numerical stability for TFLite and EdgeTPU, and formatting fixes for NCNN.
- **Documentation Updates:** Revised default models in example files and documentation for clarity and accuracy.
- **Export Order Fix:** Adjusted test order for MNN and NCNN formats to avoid CI errors on Windows systems.
- **Case-insensitive Optimizers:** Made optimizer selection process case-insensitive.
- **Auto Annotation Customization:** Added new parameters (confidence, IoU, image size) for more flexible image auto-annotation.

🎯 Purpose &amp; Impact
- **Enhanced Precision:** Improved pose estimation allows more accurate tracking of movements, crucial for applications in sports analysis, healthcare, and more.
- **Expanded Versatility:** With better export support, models can be used on more diverse hardware platforms seamlessly.
- **User Experience:** Documentation and usability updates help users gain quicker, more straightforward access to advanced features and models.
- **Platform Stability:** Ordering tests correctly means smoother application durability, especially for Windows users running CI processes.
- **Simplified Workflow:** Case-insensitive optimizer names and auto-annotation parameter flexibility reduce potential friction for users, promoting a more efficient development process.

What&#x27;s Changed
* Update `sam.md` and `sam-2.md`  by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17286
* Update examples/README.md by dme-compunet in https://github.com/ultralytics/ultralytics/pull/17284
* Patch MNN test order bug by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17290
* Case-insensitive optimizer name by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17287
* Auto annotation new parameters for SAM models  by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17288
* `ultralytics 8.3.26` EdgeTPU Pose models fix by Laughing-q in https://github.com/ultralytics/ultralytics/pull/17281


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.25...v8.3.26

8.3.25

🌟 Summary
The `v8.3.25` release of Ultralytics brings a major update with the introduction of support for the Alibaba MNN (Mobile Neural Network) format, enabling better deployment of YOLO models on mobile and embedded systems.

📊 Key Changes
- **Alibaba MNN Support**: Added the ability to export and predict with YOLO models in the MNN format.
- **Integration Updates**: Documentation and integration guides have been enhanced to include MNN details.
- **Improved ONNX Runtime**: The ONNX Runtime has been optimized for faster inference, reducing overheads and improving performance, allowing for efficient handling of dynamic and static shape inferences. 
- **Tracking Improvements**: Lowered default confidence thresholds for trackers to align with detection predictions, facilitating better tracking performance.

🎯 Purpose &amp; Impact
- **Enhanced Mobile Deployment**: With MNN support, users can deploy models with high efficiency on mobile and ARM devices, allowing for more versatile applications.
- **Performance Optimization**: Faster ONNX inference ensures reduced runtime and improved speed, which is particularly beneficial for applications requiring real-time processing.
- **User-Friendly**: Updated thresholds for tracking models now provide more intuitive operations, aligning the outputs of tracking systems with user expectations.
- **Streamlined Updates**: Improved configuration and integration steps reduce friction during the setup of modern AI workflows, enhancing overall usability and user satisfaction.

What&#x27;s Changed
* Fix arbitrary imgsz for TFLite by ambitious-octopus in https://github.com/ultralytics/ultralytics/pull/17138
* Example ORT==2.0.0-rs.5 to support onnxruntime==1.19.x by yawnBright in https://github.com/ultralytics/ultralytics/pull/16962
* Update Triton Inference Server guide by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17059
* Faster ONNX inference with bindings by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17184
* Notify only on first CI run by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17241
* Decrease default confidence threshold to start tracking new tracks by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17172
* Update publish.yml by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17251
* Pin `ray` `numpy&lt;=2.0.0` test by Laughing-q in https://github.com/ultralytics/ultralytics/pull/17245
* Update notebooks by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17260
* Fix missing argument by Laughing-q in https://github.com/ultralytics/ultralytics/pull/17253
* Update triton-inference-server.md by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17252
* Disable Ray tests by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17266
* `ultralytics 8.3.25` Alibaba MNN export and predict support by wangzhaode in https://github.com/ultralytics/ultralytics/pull/16802

New Contributors
* yawnBright made their first contribution in https://github.com/ultralytics/ultralytics/pull/16962
* wangzhaode made their first contribution in https://github.com/ultralytics/ultralytics/pull/16802

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.24...v8.3.25

8.3.24

🌟 Summary
The latest update, version 8.3.24, includes enhancements to YOLO&#x27;s prediction module, improved documentation for NVIDIA Jetson deployments, and compatibility fixes for macOS users among other changes.

📊 Key Changes
- **SAM Predict Box Enhancement**: Updated `postprocess` function to handle predictions more robustly, ensuring default bounding boxes are correctly initialized when no masks are detected.
- **Improved Documentation**: NVIDIA Jetson guide updated from YOLOv8 to YOLO11 for better deployment clarity.
- **macOS Compatibility**: Restricted `numpy` version to address compatibility issues with OpenVINO on macOS.
- **CI/CD Optimization**: Updated GitHub Actions to improve disk cleanup efficiency and streamlined CI trigger conditions.

🎯 Purpose &amp; Impact
- **Robust Predictions**: The SAM update ensures that even when no objects are detected, prediction processes do not fail, maintaining operational reliability for various applications.
- **Ease of Deployment**: Updated Jetson documentation supports users transitioning to the latest YOLO11, ensuring they leverage new capabilities for AI tasks.
- **Platform Stability**: By pinning `numpy` versions, macOS users experience fewer issues during model exports, improving user experience.
- **Efficient Development Processes**: Changes in CI/CD workflows optimize resource use, leading to faster and more cost-effective software development cycles. 🚀

These updates collectively enhance YOLO&#x27;s usability, stability, and performance in diverse environments, benefiting both developers and non-expert users.

What&#x27;s Changed
* Update OBB predict examples with boats.jpg by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17052
* Add explorer depreciation message in `datasets/index.md` by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17179
* Ultralytics Cleanup Disk action in docker.yaml by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17194
* Disable HUB CI temporarily by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17196
* Pin `numpy&lt;=2.0.0` on macOS by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17221
* Update NVIDIA Jetson Guide with YOLO11 by lakshanthad in https://github.com/ultralytics/ultralytics/pull/17206
* Fix EdgeTPU wrong PyTorch device by Skillnoob in https://github.com/ultralytics/ultralytics/pull/17199
* Adds permissions for stal

dagshub · 2024-12-02T14:53:14Z

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pyup-bot · 2024-12-09T14:53:11Z

Closing this in favor of #562

pyup-bot added 9 commits December 2, 2024 16:53

Update sphinx from 7.3.7 to 8.1.3

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Update furo from 2024.5.6 to 2024.8.6

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Update sphinx-autobuild from 2024.4.16 to 2024.10.3

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Update setuptools from 70.0.0 to 75.6.0

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Update pytest from 8.2.1 to 8.3.4

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Update pytest-env from 1.1.3 to 1.1.5

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Update fiftyone from 0.23.8 to 1.0.2

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Update datasets from 2.19.1 to 3.1.0

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Update ultralytics from 8.2.48 to 8.3.39

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pyup-bot mentioned this pull request Dec 2, 2024

Scheduled weekly dependency update for week 47 #555

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Scheduled weekly dependency update for week 48#556

Scheduled weekly dependency update for week 48#556
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pyup-bot commented Dec 2, 2024

Update sphinx from 7.3.7 to 8.1.3.

8.1.3

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8.1.1

8.1.0

Update furo from 2024.5.6 to 2024.8.6.

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Update sphinx-autobuild from 2024.4.16 to 2024.10.3.

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Update setuptools from 70.0.0 to 75.6.0.

Update pytest from 8.2.1 to 8.3.4.

Update pytest-env from 1.1.3 to 1.1.5.

1.1.5

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Update fiftyone from 0.23.8 to 1.0.2.

1.0.1

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0.25.2

0.25.1

0.25.0

0.24.1

0.24.0

Update datasets from 2.19.1 to 3.1.0.

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3.0.0

Update ultralytics from 8.2.48 to 8.3.39.

8.3.39

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