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Update Readme.md

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@ -322,10 +322,14 @@ Choose weights-file **with the highest IoU** (intersect of union) and mAP (mean
For example, **bigger IOU** 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**.
Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights`
* **IoU** (intersect of union) - average instersect of union of objects and detections for a certain threshold = 0.24 * **IoU** (intersect of union) - average instersect of union of objects and detections for a certain threshold = 0.24
* **mAP** (mean average precision) - mean value of `average precisions` for each class, where `average precision` is average value of 11 points on PR-curve for each possible threshold (each probability of detection) for the same class (Precision-Recall in terms of PascalVOC, where Precision=TP/(TP+FP) and Recall=TP/(TP+FN) ), page-11: http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc09.pdf * **mAP** (mean average precision) - mean value of `average precisions` for each class, where `average precision` is average value of 11 points on PR-curve for each possible threshold (each probability of detection) for the same class (Precision-Recall in terms of PascalVOC, where Precision=TP/(TP+FP) and Recall=TP/(TP+FN) ), page-11: http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc09.pdf
In terms of Wiki, indicators Precision and Recall have a slightly different meaning than in the PascalVOC competition, but **IoU always has the same meaning**.
![precision_recall_iou](https://hsto.org/files/ca8/866/d76/ca8866d76fb840228940dbf442a7f06a.jpg) ![precision_recall_iou](https://hsto.org/files/ca8/866/d76/ca8866d76fb840228940dbf442a7f06a.jpg)
### How to calculate mAP on PascalVOC 2007: ### How to calculate mAP on PascalVOC 2007:
@ -359,6 +363,8 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol
* for training on small objects, add the parameter `small_object=1` in the last layer [region] in your cfg-file * for training on small objects, add the parameter `small_object=1` in the last layer [region] in your cfg-file
* for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last layer [region] in your cfg-file
2. After training - for detection: 2. After training - for detection:
* Increase network-resolution by set in your `.cfg`-file (`height=608` and `width=608`) or (`height=832` and `width=832`) or (any value multiple of 32) - this increases the precision and makes it possible to detect small objects: [link](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L4) * Increase network-resolution by set in your `.cfg`-file (`height=608` and `width=608`) or (`height=832` and `width=832`) or (any value multiple of 32) - this increases the precision and makes it possible to detect small objects: [link](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L4)