From 032acd408acd119a5bd5c132765fcdd6caedea6b Mon Sep 17 00:00:00 2001 From: AlexeyAB <alexeyab84@gmail.com> Date: Tue, 22 May 2018 13:38:04 +0000 Subject: [PATCH] Experimental reinforcement learning. --- README.md | 10 +++++----- 1 files changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 3ca3412..b56299e 100644 --- a/README.md +++ b/README.md @@ -322,10 +322,10 @@ ### How to train tiny-yolo (to detect your custom objects): Do all the same steps as for the full yolo model as described above. With the exception of: -* Download default weights file for yolov2-tiny-voc: http://pjreddie.com/media/files/yolov2-tiny-voc.weights -* Get pre-trained weights yolov2-tiny-voc.conv.13 using command: `darknet.exe partial cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights yolov2-tiny-voc.conv.13 13` -* Make your custom model `yolov2-tiny-obj.cfg` based on `cfg/yolov2-tiny-voc.cfg` instead of `yolov3.cfg` -* Start training: `darknet.exe detector train data/obj.data yolov2-tiny-obj.cfg yolov2-tiny-voc.conv.13` +* Download default weights file for yolov3-tiny: https://pjreddie.com/media/files/yolov3-tiny.weights +* Get pre-trained weights `yolov3-tiny.conv.15` using command: `darknet.exe partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15` +* Make your custom model `yolov3-tiny-obj.cfg` based on `cfg/yolov3-tiny_obj.cfg` instead of `yolov3.cfg` +* Start training: `darknet.exe detector train data/obj.data yolov3-tiny-obj.cfg yolov3-tiny.conv.15` For training Yolo based on other models ([DenseNet201-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/densenet201_yolo.cfg) or [ResNet50-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/resnet50_yolo.cfg)), you can download and get pre-trained weights as showed in this file: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/partial.cmd If you made you custom model that isn't based on other models, then you can train it without pre-trained weights, then will be used random initial weights. @@ -349,7 +349,7 @@ 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**:  -- Gitblit v1.10.0