From dbdd31ee211fe8b1ac7e93ceadf7b34b8d304f34 Mon Sep 17 00:00:00 2001 From: Roland Singer <roland.singer@desertbit.com> Date: Wed, 22 Aug 2018 11:56:41 +0000 Subject: [PATCH] updated README to include information about learning rate adjustment for multiple GPUs --- README.md | 6 ++++-- 1 files changed, 4 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index f248826..18d3f15 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # Yolo-v3 and Yolo-v2 for Windows and Linux -### (neural network for object detection) +### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows) [](https://circleci.com/gh/AlexeyAB/darknet) @@ -227,7 +227,9 @@ 1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg darknet53.conv.74` -2. Then stop and by using partially-trained model `/backup/yolov3-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3` +2. Adjust the learning rate (`cfg/yolov3-voc.cfg`) to fit the amount of GPUs. The learning rate should be equal to `0.001`, regardless of how many GPUs are used for training. So `learning_rate * GPUs = 0.001`. For 4 GPUs adjust the value to `learning_rate = 0.00025`. + +3. Then stop and by using partially-trained model `/backup/yolov3-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3` https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ -- Gitblit v1.10.0