diff --git a/README.md b/README.md
index 3743b627d20..a104396cadb 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
-[](https://github.com/openvinotoolkit/openvino/releases/tag/2022.1)
+[](https://github.com/openvinotoolkit/openvino/releases/tag/2022.2.0)
[](LICENSE)
@@ -40,18 +40,18 @@ source and public models in popular formats such as TensorFlow, ONNX, PaddlePadd
### Components
* [OpenVINO™ Runtime] - is a set of C++ libraries with C and Python bindings providing a common API to deliver inference solutions on the platform of your choice.
- * [core](https://github.com/openvinotoolkit/openvino/tree/master/src/core) - provides the base API for model representation and modification.
- * [inference](https://github.com/openvinotoolkit/openvino/tree/master/src/inference) - provides an API to infer models on device.
- * [transformations](https://github.com/openvinotoolkit/openvino/tree/master/src/common/transformations) - contains the set of common transformations which are used in OpenVINO plugins.
- * [low precision transformations](https://github.com/openvinotoolkit/openvino/tree/master/src/common/low_precision_transformations) - contains the set of transformations which are used in low precision models
- * [bindings](https://github.com/openvinotoolkit/openvino/tree/master/src/bindings) - contains all available OpenVINO bindings which are maintained by OpenVINO team.
- * [c](https://github.com/openvinotoolkit/openvino/tree/master/src/bindings/c) - provides C API for OpenVINO™ Runtime
- * [python](https://github.com/openvinotoolkit/openvino/tree/master/src/bindings/python) - Python API for OpenVINO™ Runtime
-* [Plugins](https://github.com/openvinotoolkit/openvino/tree/master/src/plugins) - contains OpenVINO plugins which are maintained in open-source by OpenVINO team. For more information, see the [list of supported devices](#supported-hardware-matrix).
-* [Frontends](https://github.com/openvinotoolkit/openvino/tree/master/src/frontends) - contains available OpenVINO frontends which allow to read model from native framework format.
+ * [core](./src/core) - provides the base API for model representation and modification.
+ * [inference](./src/inference) - provides an API to infer models on the device.
+ * [transformations](./src/common/transformations) - contains the set of common transformations which are used in OpenVINO plugins.
+ * [low precision transformations](./src/common/low_precision_transformations) - contains the set of transformations that are used in low precision models
+ * [bindings](./src/bindings) - contains all available OpenVINO bindings which are maintained by the OpenVINO team.
+ * [c](./src/bindings/c) - C API for OpenVINO™ Runtime
+ * [python](./src/bindings/python) - Python API for OpenVINO™ Runtime
+* [Plugins](./src/plugins) - contains OpenVINO plugins which are maintained in open-source by the OpenVINO team. For more information, take a look at the [list of supported devices](#supported-hardware-matrix).
+* [Frontends](./src/frontends) - contains available OpenVINO frontends that allow reading models from the native framework format.
* [Model Optimizer] - is a cross-platform command-line tool that facilitates the transition between training and deployment environments, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices.
* [Post-Training Optimization Tool] - is designed to accelerate the inference of deep learning models by applying special methods without model retraining or fine-tuning, for example, post-training 8-bit quantization.
-* [Samples] - applications on C, C++ and Python languages which shows basic use cases of OpenVINO usages.
+* [Samples] - applications in C, C++ and Python languages that show basic OpenVINO use cases.
## Supported Hardware matrix
@@ -70,7 +70,7 @@ The OpenVINO™ Runtime can infer models on different hardware devices. This sec
| CPU |
Intel CPU
- | openvino_intel_cpu_plugin |
+ openvino_intel_cpu_plugin |
Intel Xeon with Intel® Advanced Vector Extensions 2 (Intel® AVX2), Intel® Advanced Vector Extensions 512 (Intel® AVX-512), and AVX512_BF16, Intel Core Processors with Intel AVX2, Intel Atom Processors with Intel® Streaming SIMD Extensions (Intel® SSE) |
@@ -81,25 +81,25 @@ The OpenVINO™ Runtime can infer models on different hardware devices. This sec
| GPU |
Intel GPU |
- openvino_intel_gpu_plugin |
+ openvino_intel_gpu_plugin |
Intel Processor Graphics, including Intel HD Graphics and Intel Iris Graphics |
| GNA |
Intel GNA |
- openvino_intel_gna_plugin |
+ openvino_intel_gna_plugin |
Intel Speech Enabling Developer Kit, Amazon Alexa* Premium Far-Field Developer Kit, Intel Pentium Silver J5005 Processor, Intel Pentium Silver N5000 Processor, Intel Celeron J4005 Processor, Intel Celeron J4105 Processor, Intel Celeron Processor N4100, Intel Celeron Processor N4000, Intel Core i3-8121U Processor, Intel Core i7-1065G7 Processor, Intel Core i7-1060G7 Processor, Intel Core i5-1035G4 Processor, Intel Core i5-1035G7 Processor, Intel Core i5-1035G1 Processor, Intel Core i5-1030G7 Processor, Intel Core i5-1030G4 Processor, Intel Core i3-1005G1 Processor, Intel Core i3-1000G1 Processor, Intel Core i3-1000G4 Processor |
| VPU |
Myriad plugin |
- openvino_intel_myriad_plugin |
+ openvino_intel_myriad_plugin |
Intel® Neural Compute Stick 2 powered by the Intel® Movidius™ Myriad™ X |
-Also OpenVINO™ Toolkit also contains several plugins which simplify loading models on several hardware devices:
+OpenVINO™ Toolkit also contains several plugins which simplify loading models on several hardware devices:
@@ -111,22 +111,22 @@ Also OpenVINO™ Toolkit also contains several plugins which simplify loading mo
| Auto |
- openvino_auto_plugin |
+ openvino_auto_plugin |
Auto plugin enables selecting Intel device for inference automatically |
| Auto Batch |
- openvino_auto_batch_plugin |
+ openvino_auto_batch_plugin |
Auto batch plugin performs on-the-fly automatic batching (i.e. grouping inference requests together) to improve device utilization, with no programming effort from the user |
| Hetero |
- openvino_hetero_plugin |
+ openvino_hetero_plugin |
Heterogeneous execution enables automatic inference splitting between several devices |
| Multi |
- openvino_auto_plugin |
+ openvino_auto_plugin |
Multi plugin enables simultaneous inference of the same model on several devices in parallel |
@@ -144,7 +144,7 @@ The latest documentation for OpenVINO™ Toolkit is available [here](https://doc
### Developer documentation
-[Developer documentation](#todo-add) contains information about architectural decisions which are applied inside the OpenVINO components.
+[Developer documentation](./docs/dev/index.md) contains information about architectural decisions which are applied inside the OpenVINO components. This documentation has all necessary information which could be needed in order to contribute to OpenVINO.
## Tutorials
@@ -169,11 +169,11 @@ The system requirements vary depending on platform and are available on dedicate
## How to build
-See the [OpenVINO Wiki](https://github.com/openvinotoolkit/openvino/wiki#how-to-build) to get more information about OpenVINO build process.
+See the [OpenVINO Wiki](https://github.com/openvinotoolkit/openvino/wiki#how-to-build) to get more information about the OpenVINO build process.
## How to contribute
-See the [CONTRIBUTING](./CONTRIBUTING.md) article for details.
+See [CONTRIBUTING](./CONTRIBUTING.md) for details. Thank you!
## Get a support
@@ -192,7 +192,7 @@ Report questions, issues and suggestions, using:
* [Intel® Distribution of OpenVINO™ toolkit Product Page](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html)
* [Intel® Distribution of OpenVINO™ toolkit Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes)
* [Neural Network Compression Framework (NNCF)](https://github.com/openvinotoolkit/nncf) - a suite of advanced algorithms for model inference optimization including quantization, filter pruning, binarization and sparsity
-* [OpenVINO™ Training Extensions (OTE)](https://github.com/openvinotoolkit/training_extensions) - a convenient environment to train Deep Learning models and convert them using OpenVINO for optimized inference.
+* [OpenVINO™ Training Extensions (OTE)](https://github.com/openvinotoolkit/training_extensions) - convenient environment to train Deep Learning models and convert them using OpenVINO for optimized inference.
* [OpenVINO™ Model Server (OVMS)](https://github.com/openvinotoolkit/model_server) - a scalable, high-performance solution for serving deep learning models optimized for Intel architectures
* [DL Workbench](https://docs.openvino.ai/2022.2/workbench_docs_Workbench_DG_Introduction.html) - an alternative, web-based version of OpenVINO designed to facilitate optimization and compression of pre-trained deep learning models.
* [Computer Vision Annotation Tool (CVAT)](https://github.com/opencv/cvat) - an online, interactive video and image annotation tool for computer vision purposes.