开源！适用于Win和Linux平台的YOLO4和YOLO3( 七 ) _Linux

Note: if error Out of memory occurs then in .cfg-file you should increase subdivisions=16, 32 or 64: link
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:

Download file with the first 29-convolutional layers of yolov4-tiny: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29 (Or get this file from yolov4-tiny.weights file by using command: darknet.exe partial cfg/yolov4-tiny.cfg yolov4-tiny.weights yolov4-tiny.conv.29 29
Make your custom model yolov4-tiny-obj.cfg based on cfg/yolov4-tiny.cfg instead of yolov4.cfg
Start training: darknet.exe detector train data/obj.data yolov4-tiny-obj.cfg yolov4-tiny.conv.29

For training Yolo based on other models (DenseNet201-Yolo or ResNet50-Yolo), you can download and get pre-trained weights as showed in this file: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/partial.cmd If you made you custom model that isn't based on other models, then you can train it without pre-trained weights, then will be used random initial weights.
When should I stop training: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:

During training, you will see varying indicators of error, and you should stop when no longer decreases 0.XXXXXXX avg:

Region Avg IOU: 0.798363, Class: 0.893232, Obj: 0.700808, No Obj: 0.004567, Avg Recall: 1.000000, count: 8 Region Avg IOU: 0.800677, Class: 0.892181, Obj: 0.701590, No Obj: 0.004574, Avg Recall: 1.000000, count: 8
9002: 0.211667, 0.60730 avg, 0.001000 rate, 3.868000 seconds, 576128 images Loaded: 0.000000 seconds

9002 - iteration number (number of batch)
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.

Once training is stopped, you should take some of last .weights-files from darknetbuilddarknet\x64backup and choose the best of them:

For example, you stopped training after 9000 iterations, but the best result can give one of previous weights (7000, 8000, 9000). It can happen due to overfitting. Overfitting - is case when you can detect objects on images from training-dataset, but can't detect objects on any others images. You should get weights from Early Stopping Point:

文章插图

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 datatrain.txt to datavalid.txt.
2.2 If training is stopped after 9000 iterations, to validate some of previous weights use this commands:
(If you use another GitHub repository, then use darknet.exe detector recall... instead of darknet.exe detector map...)

darknet.exe detector map data/obj.data yolo-obj.cfg backupyolo-obj_7000.weights
darknet.exe detector map data/obj.data yolo-obj.cfg backupyolo-obj_8000.weights
darknet.exe detector map data/obj.data yolo-obj.cfg backupyolo-obj_9000.weights

And comapre last output lines for each weights (7000, 8000, 9000):
Choose weights-file with the highest mAP (mean average precision) or IoU (intersect over union)
For example, bigger mAP gives weights yolo-obj_8000.weights - then use this weights for detection.
Or just train with -map flag:
darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -map
So you will see mAP-chart (red-line) in the Loss-chart Window. mAP will be calculated for each 4 Epochs using valid=valid.txt file that is specified in obj.data file (1 Epoch = images_in_train_txt / batch iterations)
(to change the max x-axis value - change max_batches= parameter to 2000*classes, f.e. max_batches=6000 for 3 classes)