Update Readme.md

README.md

@@ -5,12 +5,13 @@ Paper Yolo v4: https://arxiv.org/abs/2004.10934
 
 More details: http://pjreddie.com/darknet/yolo/
 
-![Darknet Continuous Integration](https://github.com/AlexeyAB/darknet/workflows/Darknet%20Continuous%20Integration/badge.svg)
+[![Darknet Continuous Integration](https://github.com/AlexeyAB/darknet/workflows/Darknet%20Continuous%20Integration/badge.svg)](https://github.com/AlexeyAB/darknet/actions?query=workflow%3A%22Darknet+Continuous+Integration%22)
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+[![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2004.10934-B31B1B.svg)](https://arxiv.org/abs/2004.10934)
 
 
 * [Requirements (and how to install dependecies)](#requirements)
@@ -382,7 +383,7 @@ Training Yolo v4 (and v3):
 
   * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L3)
   * change line subdivisions to [`subdivisions=16`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4)
-  * change line max_batches to (`classes*2000` but not less than `4000`), f.e. [`max_batches=6000`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20) if you train for 3 classes
+  * change line max_batches to (`classes*2000` but not less than number of training images, and not less than `6000`), f.e. [`max_batches=6000`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20) if you train for 3 classes
   * change line steps to 80% and 90% of max_batches, f.e. [`steps=4800,5400`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L22)
   * set network size `width=416 height=416` or any value multiple of 32: https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L8-L9
   * change line `classes=80` to your number of objects in each of 3 `[yolo]`-layers:
@@ -504,7 +505,7 @@ If you made you custom model that isn't based on other models, then you can trai
  
 ## When should I stop training:
 
-Usually sufficient 2000 iterations for each class(object), but not less than 4000 iterations in total. But for a more precise definition when you should stop training, use the following manual:
+Usually sufficient 2000 iterations for each class(object), but not less than number of training images and not less than 6000 iterations in total. But for a more precise definition when you should stop training, use the following manual:
 
 1. During training, you will see varying indicators of error, and you should stop when no longer decreases **0.XXXXXXX avg**:
 
@@ -518,6 +519,8 @@ Usually sufficient 2000 iterations for each class(object), but not less than 400
   * **0.60730 avg** - average loss (error) - **the lower, the better**
 
   When you see that average loss **0.xxxxxx avg** no longer decreases at many iterations then you should stop training. The final avgerage loss can be from `0.05` (for a small model and easy dataset) to `3.0` (for a big model and a difficult dataset).
+  
+  Or if you train with flag `-map` then you will see mAP indicator `Last accuracy mAP@0.5 = 18.50%` in the console - this indicator is better than Loss, so train while mAP increases. 
 
 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:
 
@@ -646,28 +649,6 @@ Different tools for marking objects in images:
 5. in JavaScript: https://github.com/opencv/cvat
 
 
-## Using Yolo9000
-
- Simultaneous detection and classification of 9000 objects: `darknet.exe detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights data/dog.jpg`
-
-* `yolo9000.weights` - (186 MB Yolo9000 Model) requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo9000.weights
-
-* `yolo9000.cfg` - cfg-file of the Yolo9000, also there are paths to the `9k.tree` and `coco9k.map`  https://github.com/AlexeyAB/darknet/blob/617cf313ccb1fe005db3f7d88dec04a04bd97cc2/cfg/yolo9000.cfg#L217-L218
-
-    * `9k.tree` - **WordTree** of 9418 categories  - `<label> <parent_it>`, if `parent_id == -1` then this label hasn't parent: https://raw.githubusercontent.com/AlexeyAB/darknet/master/build/darknet/x64/data/9k.tree
-
-    * `coco9k.map` - map 80 categories from MSCOCO to WordTree `9k.tree`: https://raw.githubusercontent.com/AlexeyAB/darknet/master/build/darknet/x64/data/coco9k.map
-
-* `combine9k.data` - data file, there are paths to: `9k.labels`, `9k.names`, `inet9k.map`, (change path to your `combine9k.train.list`): https://raw.githubusercontent.com/AlexeyAB/darknet/master/build/darknet/x64/data/combine9k.data
-
-    * `9k.labels` - 9418 labels of objects: https://raw.githubusercontent.com/AlexeyAB/darknet/master/build/darknet/x64/data/9k.labels
-
-    * `9k.names` -
-9418 names of objects: https://raw.githubusercontent.com/AlexeyAB/darknet/master/build/darknet/x64/data/9k.names
-
-    * `inet9k.map` - map 200 categories from ImageNet to WordTree `9k.tree`: https://raw.githubusercontent.com/AlexeyAB/darknet/master/build/darknet/x64/data/inet9k.map
-
-
 ## How to use Yolo as DLL and SO libraries
 
 * on Linux