diff --git a/CHANGELOG.md b/CHANGELOG.md index 11aeb614a0e..4c6aaebb02f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,16 @@ All notable changes to this project will be documented in this file. +## \[v1.4.2\] + +### Enhancements + +- Add model category attributes to model template () + +### Bug fixes + +- Add workaround for the incorrect meta info M-RCNN (used for XAI) () + ## \[v1.4.1\] ### Enhancements @@ -255,7 +265,7 @@ All notable changes to this project will be documented in this file. - Enhance `find` command to find configurations of supported tasks / algorithms / models / backbones - Introduce `build` command to customize task or model configurations in isolated workspace - Auto-config feature to automatically select the right algorithm and default model for the `train` & `build` command by detecting the task type of given input dataset -- Improve [documentation](https://openvinotoolkit.github.io/training_extensions/1.4.1/guide/get_started/introduction.html) +- Improve [documentation](https://openvinotoolkit.github.io/training_extensions/1.0.0/guide/get_started/introduction.html) - Improve training performance by introducing enhanced loss for the few-shot transfer ### Bug fixes diff --git a/README.md b/README.md index 8a15d7a590f..a75f8436fa1 100644 --- a/README.md +++ b/README.md @@ -5,8 +5,8 @@ --- [Key Features](#key-features) -[Installation](https://openvinotoolkit.github.io/training_extensions/1.4.1/guide/get_started/installation.html) -[Documentation](https://openvinotoolkit.github.io/training_extensions/1.4.1/index.html) +[Installation](https://openvinotoolkit.github.io/training_extensions/1.4.2/guide/get_started/installation.html) +[Documentation](https://openvinotoolkit.github.io/training_extensions/1.4.2/index.html) [License](#license) [![PyPI](https://img.shields.io/pypi/v/otx)](https://pypi.org/project/otx) @@ -54,7 +54,7 @@ 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/1.4.1/guide/explanation/algorithms/index.html): +OpenVINO™ Training Extensions supports the [following learning methods](https://openvinotoolkit.github.io/training_extensions/1.4.2/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** @@ -64,9 +64,9 @@ 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/1.4.1/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/1.4.2/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/v1.4.1/index.html) 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/1.4.1/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/1.4.2/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. --- @@ -74,7 +74,7 @@ OpenVINO™ Training Extensions will provide the following features in coming re ### Installation -Please refer to the [installation guide](https://openvinotoolkit.github.io/training_extensions/1.4.1/guide/get_started/installation.html). +Please refer to the [installation guide](https://openvinotoolkit.github.io/training_extensions/1.4.2/guide/get_started/installation.html). Note: Python 3.8 and 3.9 were tested, along with Ubuntu 18.04 and 20.04. @@ -90,7 +90,7 @@ Note: Python 3.8 and 3.9 were tested, along with Ubuntu 18.04 and 20.04. - `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/1.4.1/guide/get_started/cli_commands.html). +You can find more details with examples in the [CLI command intro](https://openvinotoolkit.github.io/training_extensions/1.4.2/guide/get_started/cli_commands.html). --- diff --git a/docs/source/conf.py b/docs/source/conf.py index 4c46b04d59b..32bc942b667 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -18,7 +18,7 @@ project = 'OpenVINO™ Training Extensions' copyright = '2023, OpenVINO™ Training Extensions Contributors' author = 'OpenVINO™ Training Extensions Contributors' -release = '1.4.1' +release = '1.4.2' # -- General configuration --------------------------------------------------- # diff --git a/src/otx/__init__.py b/src/otx/__init__.py index 8bfc6e5b9de..76a1873f9e7 100644 --- a/src/otx/__init__.py +++ b/src/otx/__init__.py @@ -3,5 +3,5 @@ # Copyright (C) 2021-2023 Intel Corporation # SPDX-License-Identifier: Apache-2.0 -__version__ = "1.4.1" +__version__ = "1.4.2rc1" # NOTE: Sync w/ src/otx/api/usecases/exportable_code/demo/requirements.txt on release diff --git a/src/otx/api/usecases/exportable_code/demo/requirements.txt b/src/otx/api/usecases/exportable_code/demo/requirements.txt index ec9bf79a597..da1676a7aed 100644 --- a/src/otx/api/usecases/exportable_code/demo/requirements.txt +++ b/src/otx/api/usecases/exportable_code/demo/requirements.txt @@ -1,4 +1,4 @@ openvino==2023.0 openvino-model-api==0.1.3 -otx==1.4.1 +otx==1.4.2rc1 numpy>=1.21.0,<=1.23.5 # np.bool was removed in 1.24.0 which was used in openvino runtime