| | |
|---|
| | | |
|---|
| | | **Note:** If you changed width= or height= in your cfg-file, then new width and height must be divisible by 32. |
|---|
| | | |
|---|
| | | **Note:** After training use such command for detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights` |
|---|
| | | |
|---|
| | | ### 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: |
|---|
| | |
|---|
| | | |
|---|
| | | * check that each object are mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark |
|---|
| | | |
|---|
| | | * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 images for each class or more |
|---|
| | | * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train `2000*classes` iterations or more |
|---|
| | | |
|---|
| | | * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files) - use as many images of negative samples as there are images with objects |
|---|
| | | |
|---|
