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MahmudulAlam / Automatic-Identification-and-Counting-of-Blood-Cells

Machine learning approach of automatic identification and counting of blood cells (RBC, WBC, and Platelet) with KNN and IOU based verification.
https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/htl.2018.5098
GNU General Public License v3.0
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training #22

Closed zezeeze closed 2 months ago

zezeeze commented 5 months ago

The loss during model training is 10-20, how to solve it? Can YOLOv7 or higher versions be used?

MahmudulAlam commented 2 months ago

@zezeeze

The value of the loss can be subjective. What is your accuracy rate?

zezeeze commented 2 months ago

The accuracy rate is more than 85%, and the effect is good. So although the loss is high, it does not affect? Another problem is the checkpoint,.index,.meta,.profile and.data-00000-of-00001 files generated in the weights folder after my training. How do I continue training on the basis of the new data? I sought the help of chatgpt and related students, but did not solve the problem. Thank you.

MahmudulAlam commented 2 months ago

@zezeeze The size of the loss value sometimes depends on the definition. If you define by loss = sum(error), the number would be higher compared to loss = mean(error). If you want to improve performance, use more data, apply data augmentation, etc. I don't think loss value would help you here.

To resume/continue training use the checkpoint number after --load checkpoint_num 

python flow --model cfg/tiny-yolo-voc-3c.cfg --load 1500 --train --gpu .7 --annotation dataset/Training/Annotations --dataset dataset/Training/Images --lr 1e-3 --epoch 100
zezeeze commented 2 months ago

Thank you so much for your help ！！！！ I worked it out the way you said.