OrgLion-ML
/
darknet
mirror of https://github.com/AlexeyAB/darknet.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

Update Readme.md

This commit is contained in:
Alexey 2019-11-09 14:03:29 +03:00 committed by GitHub
parent 0fa9c8f105
commit 0eee8404bf
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 2 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
README.md
View File

@ -607,6 +607,8 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol
* each: `model of object, side, illimination, scale, each 30 grad` of the turn and inclination angles - these are *different objects* from an internal perspective of the neural network. So the more *different objects* you want to detect, the more complex network model should be used.
* to make the detected bounded boxes more accurate, you can add 3 parameters `ignore_thresh = .9 iou_normalizer=0.5 iou_loss=giou` to each `[yolo]` layer and train, it will increase mAP@0.9, but decrease mAP@0.5.
* Only if you are an **expert** in neural detection networks - recalculate anchors for your dataset for `width` and `height` from cfg-file:
`darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416`
then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file. But you should change indexes of anchors `masks=` for each [yolo]-layer, so that 1st-[yolo]-layer has anchors larger than 60x60, 2nd larger than 30x30, 3rd remaining. Also you should change the `filters=(classes + 5)*<number of mask>` before each [yolo]-layer. If many of the calculated anchors do not fit under the appropriate layers - then just try using all the default anchors.