IntenseWebs/openvino
3
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Update model_optimization_guide.md (#18954)

Browse Source
This commit is contained in:
Maciej Smyk
2023-08-04 14:49:52 +02:00
committed by GitHub
parent c32b9a0cd5
commit 9c10e33fc7
1 changed files with 1 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
docs/optimization_guide/model_optimization_guide.md
View File

@@ -16,7 +16,7 @@ Model optimization is an optional offline step of improving the final model perf
- :doc:`Training-time Optimization <tmo_introduction>`, a suite of advanced methods for training-time model optimization within the DL framework, such as PyTorch and TensorFlow 2.x. It supports methods like Quantization-aware Training, Structured and Unstructured Pruning, etc.
.. note:: OpenVINO also supports optimized models (for example, quantized) from source frameworks such as PyTorch, TensorFlow, and ONNX (in Q/DQ format). No special steps are required in this case and optimized models can be converted to the OpenVINO Intermediate Representation format (IR) right away.
.. note:: OpenVINO also supports optimized models (for example, quantized) from source frameworks such as PyTorch, TensorFlow, and ONNX (in Q/DQ; Quantize/DeQuantize format). No special steps are required in this case and optimized models can be converted to the OpenVINO Intermediate Representation format (IR) right away.
Post-training Quantization is the fastest way to optimize a model and should be applied first, but it is limited in terms of achievable accuracy-performance trade-off. In case of poor accuracy or performance after Post-training Quantization, Training-time Optimization can be used as an option.