From ae9fb2abbd8e2b7e079b56bf1cf39754d7c091ac Mon Sep 17 00:00:00 2001 From: Nadeen Udantha Date: Fri, 18 May 2018 14:19:19 +0530 Subject: [PATCH] ojbects -> objects --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 3ca3412f..352edf52 100644 --- a/README.md +++ b/README.md @@ -349,7 +349,7 @@ Usually sufficient 2000 iterations for each class(object). But for a more precis 2. Once training is stopped, you should take some of last `.weights`-files from `darknet\build\darknet\x64\backup` and choose the best of them: -For example, you stopped training after 9000 iterations, but the best result can give one of previous weights (7000, 8000, 9000). It can happen due to overfitting. **Overfitting** - is case when you can detect objects on images from training-dataset, but can't detect ojbects on any others images. You should get weights from **Early Stopping Point**: +For example, you stopped training after 9000 iterations, but the best result can give one of previous weights (7000, 8000, 9000). It can happen due to overfitting. **Overfitting** - is case when you can detect objects on images from training-dataset, but can't detect objects on any others images. You should get weights from **Early Stopping Point**: ![Overfitting](https://hsto.org/files/5dc/7ae/7fa/5dc7ae7fad9d4e3eb3a484c58bfc1ff5.png)