.. ENOT reference documentation master file.

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ENOT Reference Documentation
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You can find ENOT reference documentation here. If you experience lack of
documentation, you probably should contact ENOT team to complete it or to make
it more clear.

**Before proceeding to examples or documentation, we recommend you to read this
page carefully.**

ENOT framework supports
`quantization <https://arxiv.org/abs/2106.08295>`_ and
`pruning <https://arxiv.org/abs/2003.03033>`_.

**Neural network quantization** attempts to make weights of neural network discrete
(instead of using their floating-point representation, usually float32 format).
Quantization decreases model size by a factor of 4 by using int8 data type.
On NVIDIA GPUs and Intel CPUs quantization may also provide noticeable
inference time decrease.

* :ref:`Tutorial - automatic quantization for enot-lite <E1 ref>`
* :ref:`Tutorial - Ultralytics YOLOv5 quantization <E4 ref>`

**Neural network pruning** removes redundant (or least important) channels (or
features) from neural network. Our framework implements structured pruning
(removing channels or neurons, not separate connections).

* :ref:`Tutorial - pruning <E2 ref>`
* :ref:`Tutorial - pruning (manual) <E3 ref>`
* :ref:`Tutorial - label selector starting points <E5 ref>`

To improve metrics of baseline or to finetune model it might be helpful to try our optimizer.

* :ref:`Tutorial - ENOT baseline optimizer <E4 ref>`

Before reading the documentation, we recommend you to look at the
:ref:`ENOT Tutorials` to clarify basic notions and concepts of the framework.


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Table of contents
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Packages are listed in descending importance order.

.. toctree::
   :maxdepth: 1

   Pruning <pruning>
   Pruning v2 <pruning_v2>
   Quantization <quantization>
   Rescaling <rescaling>
   Distillation <distillation>
   Optimization <optimization>
   Utils <utils>