diff --git a/README.md b/README.md
index cc575da5..5d6f777c 100644
--- a/README.md
+++ b/README.md
@@ -521,10 +521,10 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an
 ### 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 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`
+* Download file with the first 29-convolutional layers of yolov4-tiny: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29
+ (Or get this file from yolov4-tiny.weights file by using command: `darknet.exe partial cfg/yolov5-tiny.cfg yolov5-tiny.weights yolov5-tiny.conv.29 29`
+* Make your custom model `yolov4-tiny-obj.cfg` based on `cfg/yolov4-tiny.cfg` instead of `yolov4.cfg`
+* Start training: `darknet.exe detector train data/obj.data yolov4-tiny-obj.cfg yolov4-tiny.conv.29`
 
 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.