From 1411cb1358adae90bd159c42a6be3e605a8db432 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?O=C4=9Fuzhan=20B=C3=BCy=C3=BCksolak?= Date: Fri, 6 Dec 2024 17:24:53 +0300 Subject: [PATCH] ReadMe Update (#357) --- README.md | 4 ++-- README.pdf | Bin 4323333 -> 4323408 bytes 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index f2ede89e..7aba1007 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # ADI MAX78000/MAX78002 Model Training and Synthesis -August 27, 2024 +November 7, 2024 **Note: This branch requires PyTorch 2. Please see the archive-1.8 branch for PyTorch 1.8 support. [KNOWN_ISSUES](KNOWN_ISSUES.txt) contains a list of known issues.** @@ -1636,7 +1636,7 @@ Quantization-aware training can be disabled by specifying `--qat-policy N The proper choice of `start_epoch` is important for achieving good results, and the default policy’s `start_epoch` may be much too small. As a rule of thumb, set `start_epoch` to a very high value (e.g., 1000) to begin, and then observe where in the training process the model stops learning. This epoch can be used as `start_epoch`, and the final network metrics (after an additional number of epochs) should be close to the non-QAT metrics. *Additionally, ensure that the learning rate after the `start_epoch` epoch is relatively small.* -For more information, please also see [Quantization](#quantization). +For more information, please also see [Quantization](#quantization) and [QATv2](https://github.com/analogdevicesinc/ai8x-training/blob/develop/docs/QATv2.md). #### Batch Normalization diff --git a/README.pdf b/README.pdf index b426a1f14acd6667522948ac3ad39e0e7fade739..c954703df1c6947a564a5500807eaa06e97f4584 100644 GIT binary patch delta 63652 zcmY(|V{j(XngHO~*2K0Zwv&l%Pi)&?Y}>YN+qP{RlVo%6?w|ef)Twr#s;<*7f^I`V zW`D%;09ax8_+T8J9E|m?VcasWcsHWAIHGS(X#REfS`22000)4V$1C&CSowbXUdpm# zBmoz{eEWeLkL7z`I+m4JlO~)w6;0>Sg|o+@{ow{KTl_BO7&_Yt_$ZGQbbh~odW=C8 zfT1c%w09stCJV+SkqvAE6Q`2v`JOrJ`Lcfh0Gts}!1yZGV4vSv6Rf1vP^4bY`~{>P zBldiIyf6G_`FsaF2|;8L8f5L-mdNKPj(vZw{4I9ez3ItDkqy(2k0-x03^Th2tkh}k 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