Update Readme.md

README.md

@@ -169,9 +169,9 @@ Then add to your created project:
 
 5. Run command: `type 2007_train.txt 2007_val.txt 2012_*.txt > train.txt`
 
-6. Set `batch=64` and `subdivisions=8` in the file `yolo-voc.cfg`: [link](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.cfg#L3)
+6. Set `batch=64` and `subdivisions=8` in the file `yolo-voc.2.0.cfg`: [link](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.cfg#L3)
 
-7. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data yolo-voc.cfg darknet19_448.conv.23`
+7. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg darknet19_448.conv.23`
 
 If required change pathes in the file `build\darknet\x64\data\voc.data`
 
@@ -179,9 +179,9 @@ More information about training by the link: http://pjreddie.com/darknet/yolo/#t
 
 ## How to train with multi-GPU:
 
-1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data yolo-voc.cfg darknet19_448.conv.23`
+1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg darknet19_448.conv.23`
 
-2. Then stop and by using partially-trained model `/backup/yolo-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data yolo-voc.cfg yolo-voc_1000.weights -gpus 0,1,2,3`
+2. Then stop and by using partially-trained model `/backup/yolo-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg yolo-voc_1000.weights -gpus 0,1,2,3`
 
 https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ
 
@@ -194,7 +194,7 @@ https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ
   * change line `classes=20` to your number of objects
   * change line #237 from [`filters=125`](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.cfg#L237) to `filters=(classes + 5)*5` (generally this depends on the `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`)
 
-  For example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolo-voc.cfg` in such lines:
+  For example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolo-voc.2.0.cfg` in such lines:
 
   ```
   [convolutional]
@@ -275,6 +275,8 @@ For example, you stopped training after 9000 iterations, but the best result can
 
 ![Overfitting](https://hsto.org/files/5dc/7ae/7fa/5dc7ae7fad9d4e3eb3a484c58bfc1ff5.png) 
 
+To get weights from Early Stopping Point:
+
   2.1. At first, in your file `obj.data` you must specify the path to the validation dataset `valid = valid.txt` (format of `valid.txt` as in `train.txt`), and if you haven't validation images, just copy `data\train.txt` to `data\valid.txt`.
 
   2.2 If training is stopped after 9000 iterations, to validate some of previous weights use this commands:
@@ -290,7 +292,7 @@ And comapre last output lines for each weights (7000, 8000, 9000):
 * **IOU** - the bigger, the better (says about accuracy) - **better to use**
 * **Recall** - the bigger, the better (says about accuracy) - actually Yolo calculates true positives, so it shouldn't be used
 
-For example, **bigger IUO** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**.
+For example, **bigger IOU** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**.
 
 
 ![precision_recall_iou](https://hsto.org/files/ca8/866/d76/ca8866d76fb840228940dbf442a7f06a.jpg)