Release 1.2.2

Signed-off-by: Songki Choi <[email protected]>

Author: Songki Choi

CHANGELOG.md

@@ -6,7 +6,7 @@ All notable changes to this project will be documented in this file.
 
 ### Enhancements
 
-- Improve warning message for tiling configurable parameter
+- Improve warning message for tiling configurable parameter (<https://github.com/openvinotoolkit/training_extensions/pull/2193>)
 
 ## \[v1.2.1\]
 

README.md

@@ -5,8 +5,8 @@
 ---
 
 [Key Features](#key-features) •
-[Quick Start](https://openvinotoolkit.github.io/training_extensions/1.2.1/guide/get_started/quick_start_guide/index.html) •
-[Documentation](https://openvinotoolkit.github.io/training_extensions/1.2.1/index.html) •
+[Quick Start](https://openvinotoolkit.github.io/training_extensions/1.2.2/guide/get_started/quick_start_guide/index.html) •
+[Documentation](https://openvinotoolkit.github.io/training_extensions/1.2.2/index.html) •
 [License](#license)
 
 [![PyPI](https://img.shields.io/pypi/v/otx)](https://pypi.org/project/otx)
@@ -55,7 +55,7 @@ OpenVINO™ Training Extensions supports the following computer vision tasks:
 - **Action recognition** including action classification and detection
 - **Anomaly recognition** tasks including anomaly classification, detection and segmentation
 
-OpenVINO™ Training Extensions supports the [following learning methods](https://openvinotoolkit.github.io/training_extensions/1.2.1/guide/explanation/algorithms/index.html):
+OpenVINO™ Training Extensions supports the [following learning methods](https://openvinotoolkit.github.io/training_extensions/1.2.2/guide/explanation/algorithms/index.html):
 
 - **Supervised**, incremental training, which includes class incremental scenario and contrastive learning for classification and semantic segmentation tasks
 - **Semi-supervised learning**
@@ -65,17 +65,17 @@ OpenVINO™ Training Extensions will provide the following features in coming re
 
 - **Distributed training** to accelerate the training process when you have multiple GPUs
 - **Half-precision training** to save GPUs memory and use larger batch sizes
-- Integrated, efficient [hyper-parameter optimization module (HPO)](https://openvinotoolkit.github.io/training_extensions/1.2.1/guide/explanation/additional_features/hpo.html). Through dataset proxy and built-in hyper-parameter optimizer, you can get much faster hyper-parameter optimization compared to other off-the-shelf tools. The hyperparameter optimization is dynamically scheduled based on your resource budget.
+- Integrated, efficient [hyper-parameter optimization module (HPO)](https://openvinotoolkit.github.io/training_extensions/1.2.2/guide/explanation/additional_features/hpo.html). Through dataset proxy and built-in hyper-parameter optimizer, you can get much faster hyper-parameter optimization compared to other off-the-shelf tools. The hyperparameter optimization is dynamically scheduled based on your resource budget.
 - OpenVINO™ Training Extensions uses [Datumaro](https://openvinotoolkit.github.io/datumaro/docs/) as the backend to hadle datasets. Thanks to that, OpenVINO™ Training Extensions supports the most common academic field dataset formats for each task. We constantly working to extend supported formats to give more freedom of datasets format choice.
-- [Auto-configuration functionality](https://openvinotoolkit.github.io/training_extensions/1.2.1/guide/explanation/additional_features/auto_configuration.html). OpenVINO™ Training Extensions analyzes provided dataset and selects the proper task and model template to provide the best accuracy/speed trade-off. It will also make a random auto-split of your dataset if there is no validation set provided.
+- [Auto-configuration functionality](https://openvinotoolkit.github.io/training_extensions/1.2.2/guide/explanation/additional_features/auto_configuration.html). OpenVINO™ Training Extensions analyzes provided dataset and selects the proper task and model template to provide the best accuracy/speed trade-off. It will also make a random auto-split of your dataset if there is no validation set provided.
 
 ---
 
 ## Getting Started
 
 ### Installation
 
-Please refer to the [installation guide](https://openvinotoolkit.github.io/training_extensions/1.2.1/guide/get_started/quick_start_guide/installation.html).
+Please refer to the [installation guide](https://openvinotoolkit.github.io/training_extensions/1.2.2/guide/get_started/quick_start_guide/installation.html).
 
 ### OpenVINO™ Training Extensions CLI Commands
 
@@ -89,7 +89,7 @@ Please refer to the [installation guide](https://openvinotoolkit.github.io/train
 - `otx demo` allows one to apply a trained model on the custom data or the online footage from a web camera and see how it will work in a real-life scenario.
 - `otx explain` runs explain algorithm on the provided data and outputs images with the saliency maps to show how your model makes predictions.
 
-You can find more details with examples in the [CLI command intro](https://openvinotoolkit.github.io/training_extensions/1.2.1/guide/get_started/quick_start_guide/cli_commands.html).
+You can find more details with examples in the [CLI command intro](https://openvinotoolkit.github.io/training_extensions/1.2.2/guide/get_started/quick_start_guide/cli_commands.html).
 
 ---
 

docs/source/conf.py

@@ -18,7 +18,7 @@
 project = 'OpenVINO™ Training Extensions'
 copyright = '2023, OpenVINO™ Training Extensions Contributors'
 author = 'OpenVINO™ Training Extensions Contributors'
-release = '1.2.1'
+release = '1.2.2'
 
 # -- General configuration --------------------------------------------------- #