python - What all these parameters means in yoloV4 model

Question

What do all of these parameters from training YOLOv4 mean?

(next mAP calculation at 1300 iterations)

Last accuracy [email protected] = 63.16 %, best = 68.55 %

1249: 26.351213, 24.018257 avg loss, 0.001000 rate, 2.983998 seconds, 39968 images, 10.505599 hours left Loaded: 0.000068 seconds

(next mAP calculation at 1300 iterations) Last accuracy [email protected] = 63.16 %, best = 68.55 %

1250: 13.904115, 23.006844 avg loss, 0.001000 rate, 4.093653 seconds, 40000 images, 10.456502 hours left Resizing, random_coef = 1.40

question from:https://stackoverflow.com/questions/66048020/what-all-these-parameters-means-in-yolov4-model

Answer 1

Here's what the parameters mean.

For your given example:

(next mAP calculation at 1300 iterations) Last accuracy [email protected] = 63.16 %, best = 68.55 %

1250: 13.904115, 23.006844 avg loss, 0.001000 rate, 4.093653 seconds, 40000 images, 10.456502 hours left Resizing, random_coef = 1.40

• 1250 --> iteration

• Last accuracy [email protected] --> Last mean average precision (mAP) at 50% IoU threshold. mAP is calculated every 100th iteration. So, in the example, it's the mAP from iteration = 1200

• best --> highest mAP so far

• 13.904115 --> total loss

• 23.006844 avg loss--> average loss, this is the thing you should care about for being low in training

• 0.001000 rate --> learning rate

• 4.093653 seconds --> total time spent to process the batch

• 40000 images --> total amount of images used during training so far (iteration*batch = 1250 * 32)

• 10.456502 hours left --> estimated time remaining for finishing up to the max_batches in your config file

• Resizing, random_coef = 1.40 --> Confirming that your dataset is being randomly resized every 10 iterations from 1/1.4 to 1.4 (in this iteration, it's 1.40)

References: https://github.com/AlexeyAB/darknet/blob/master/src/detector.c https://github.com/AlexeyAB/darknet/wiki/CFG-Parameters-in-the-different-layers