diff --git a/README.md b/README.md
index 66e063ba322..9929b92255c 100644
--- a/README.md
+++ b/README.md
@@ -8,70 +8,76 @@
[Usage](#usage) •
[Tutorials and Samples](#demos-tutorials-and-samples) •
[Third-party integration](#third-party-repository-integration) •
-[Model Zoo](./docs/ModelZoo.md)
+[NNCF Model Zoo](./docs/ModelZoo.md)
[](https://github.com/openvinotoolkit/nncf/releases)
-[](https://docs.openvino.ai/latest/openvino_docs_model_optimization_guide.html)
+[](https://docs.openvino.ai/nncf)
[](LICENSE)
[](https://pypi.org/project/nncf/)
-Neural Network Compression Framework (NNCF) provides a suite of post-training and training-time algorithms for neural networks inference optimization in [OpenVINO™](https://docs.openvino.ai) with minimal accuracy drop.
+Neural Network Compression Framework (NNCF) provides a suite of post-training and training-time algorithms for optimizing inference of neural networks in [OpenVINO™](https://docs.openvino.ai) with a minimal accuracy drop.
-NNCF is designed to work with models from [PyTorch](https://pytorch.org/), [TensorFlow](https://www.tensorflow.org/), [ONNX](https://onnx.ai/) and [OpenVINO™](https://docs.openvino.ai/latest/home.html).
+NNCF is designed to work with models from [PyTorch](https://pytorch.org/), [TensorFlow](https://www.tensorflow.org/), [ONNX](https://onnx.ai/) and [OpenVINO™](https://docs.openvino.ai).
-NNCF provides [samples](#demos-tutorials-and-samples) that demonstrate the usage of compression algorithms for different use cases and models. See compression results achievable with the NNCF-powered samples at [Model Zoo page](./docs/ModelZoo.md).
+NNCF provides [samples](#demos-tutorials-and-samples) that demonstrate the usage of compression algorithms for different use cases and models. See compression results achievable with the NNCF-powered samples on the [NNCF Model Zoo page](./docs/ModelZoo.md).
The framework is organized as a Python\* package that can be built and used in a standalone mode. The framework
architecture is unified to make it easy to add different compression algorithms for both PyTorch and TensorFlow deep
learning frameworks.
+
+
## Key Features
### Post-Training Compression Algorithms
-| Compression algorithm |OpenVINO|PyTorch| TensorFlow | ONNX |
-|:----------------------------------------------------------------------------| :---: | :---: |:--------:|:------------------:|
-| [Post-Training Quantization](./docs/usage/post_training_compression/post_training_quantization/Usage.md) | Supported | Supported |Supported| Supported |
-| [Weights Compression](./docs/usage/post_training_compression/weights_compression/Usage.md) | Supported | Supported |Not supported| Not supported |
+| Compression algorithm | OpenVINO | PyTorch | TensorFlow | ONNX |
+| :------------------------------------------------------------------------------------------------------- | :-------: | :-------: | :-----------: | :-----------: |
+| [Post-Training Quantization](./docs/usage/post_training_compression/post_training_quantization/Usage.md) | Supported | Supported | Supported | Supported |
+| [Weights Compression](./docs/usage/post_training_compression/weights_compression/Usage.md) | Supported | Supported | Not supported | Not supported |
### Training-Time Compression Algorithms
-|Compression algorithm|PyTorch|TensorFlow|
-| :--- | :---: | :---: |
-|[Quantization Aware Training](./docs/usage/training_time_compression/quantization_aware_training/Usage.md) | Supported | Supported |
-|[Mixed-Precision Quantization](./docs/usage/training_time_compression/other_algorithms/LegacyQuantization.md#mixed-precision-quantization) | Supported | Not supported |
-|[Sparsity](./docs/usage/training_time_compression/other_algorithms/Sparsity.md) | Supported | Supported |
-|[Filter pruning](./docs/usage/training_time_compression/other_algorithms/Pruning.md) | Supported | Supported |
-|[Movement pruning](./nncf/experimental/torch/sparsity/movement/MovementSparsity.md) | Experimental | Not supported |
+| Compression algorithm | PyTorch | TensorFlow |
+| :--------------------------------------------------------------------------------------------------------- | :----------: | :-----------: |
+| [Quantization Aware Training](./docs/usage/training_time_compression/quantization_aware_training/Usage.md) | Supported | Supported |
+| [Mixed-Precision Quantization](./docs/usage/training_time_compression/other_algorithms/LegacyQuantization.md#mixed-precision-quantization) | Supported | Not supported |
+| [Sparsity](./docs/usage/training_time_compression/other_algorithms/Sparsity.md) | Supported | Supported |
+| [Filter pruning](./docs/usage/training_time_compression/other_algorithms/Pruning.md) | Supported | Supported |
+| [Movement pruning](./nncf/experimental/torch/sparsity/movement/MovementSparsity.md) | Experimental | Not supported |
- Automatic, configurable model graph transformation to obtain the compressed model.
- > **NOTE**: Limited support for TensorFlow models. The models created using Sequential or Keras Functional API are only supported.
+ > **NOTE**: Limited support for TensorFlow models. Only models created using Sequential or Keras Functional API are supported.
- Common interface for compression methods.
- GPU-accelerated layers for faster compressed model fine-tuning.
- Distributed training support.
-- Git patch for prominent third-party repository ([huggingface-transformers](https://github.com/huggingface/transformers)) demonstrating the process of integrating NNCF into custom training pipelines
-- Seamless combination of pruning, sparsity and quantization algorithms. Please refer to [optimum-intel](https://github.com/huggingface/optimum-intel/tree/main/examples/openvino) for examples of
-joint (movement) pruning, quantization and distillation (JPQD), end-to-end from NNCF optimization to compressed OpenVINO IR.
+- Git patch for prominent third-party repository ([huggingface-transformers](https://github.com/huggingface/transformers)) demonstrating the process of integrating NNCF into custom training pipelines.
+- Seamless combination of pruning, sparsity, and quantization algorithms. Please refer to [optimum-intel](https://github.com/huggingface/optimum-intel/tree/main/examples/openvino) for examples of
+joint (movement) pruning, quantization, and distillation (JPQD), end-to-end from NNCF optimization to compressed OpenVINO IR.
- Exporting PyTorch compressed models to ONNX\* checkpoints and TensorFlow compressed models to SavedModel or Frozen Graph format, ready to use with [OpenVINO™ toolkit](https://docs.openvino.ai).
- Support for [Accuracy-Aware model training](./docs/usage/training_time_compression/other_algorithms/Usage.md#accuracy-aware-model-training) pipelines via the [Adaptive Compression Level Training](./docs/accuracy_aware_model_training/AdaptiveCompressionLevelTraining.md) and [Early Exit Training](./docs/accuracy_aware_model_training/EarlyExitTraining.md).
+
+
## Documentation
This documentation covers detailed information about NNCF algorithms and functions needed for the contribution to NNCF.
-The latest user documentation for NNCF is available [here](https://docs.openvino.ai/latest/openvino_docs_model_optimization_guide.html).
+The latest user documentation for NNCF is available [here](https://docs.openvino.ai/nncf).
NNCF API documentation can be found [here](https://openvinotoolkit.github.io/nncf/autoapi/nncf/).
+
+
## Usage
### Post-Training Quantization
The NNCF PTQ is the simplest way to apply 8-bit quantization. To run the algorithm you only need your model and a small (~300 samples) calibration dataset.
-[OpenVINO](https://github.com/openvinotoolkit/openvino) is the preferred backend to run PTQ with, and PyTorch, TensorFlow and ONNX are also supported.
+[OpenVINO](https://github.com/openvinotoolkit/openvino) is the preferred backend to run PTQ with, while PyTorch, TensorFlow, and ONNX are also supported.
OpenVINO
@@ -127,7 +133,7 @@ quantized_model = nncf.quantize(model, calibration_dataset)
```
-**NOTE** In case the Post-Training Quantization algorithm could not reach quality requirements you can fine-tune the quantized pytorch model. Example of the Quantization-Aware training pipeline for a pytorch model could be found [here](examples/quantization_aware_training/torch/resnet18/README.md).
+**NOTE** If the Post-Training Quantization algorithm does not meet quality requirements you can fine-tune the quantized pytorch model. You can find an example of the Quantization-Aware training pipeline for a pytorch model [here](examples/quantization_aware_training/torch/resnet18/README.md).
PyTorch
@@ -244,7 +250,7 @@ model.load_state_dict(state_dict)
### Training-Time Compression
-Below is an example of Accuracy Aware RB Sparsification pipeline where model weights and compression parameters may be fine-tuned to achieve a higher accuracy.
+Here is an example of Accuracy Aware RB Sparsification pipeline where model weights and compression parameters may be fine-tuned to achieve a higher accuracy.
PyTorch
@@ -281,7 +287,7 @@ compression_ctrl.export_model("compressed_model.onnx")
torch.save(compressed_model.state_dict(), "compressed_model.pth")
```
-**NOTE (PyTorch)**: Due to the way NNCF works within the PyTorch backend, `import nncf` must be done before any other import of `torch` in your package _or_ in third-party packages that your code utilizes, otherwise the compression may be applied incompletely.
+**NOTE (PyTorch)**: Due to the way NNCF works within the PyTorch backend, `import nncf` must be done before any other import of `torch` in your package _or_ in third-party packages that your code utilizes. Otherwise, the compression may be applied incompletely.