Table 2 Model training and validation logs.

From: Software application in early blight detection in tomatoes using modified MobileNet architecture

Epoch

Loss

Accuracy

Precision

Recall

F1-score

Val. loss

Val. accuracy

Val. precision

1

0.5890

0.5475

1.0000

0.1216

0.6800

0.5302

0.5347

1.0000

2

0.5112

0.8449

1.0000

0.6990

0.6800

0.5058

0.8107

1.0000

3

0.4808

0.9281

1.0000

0.8604

0.6800

0.4874

0.9041

1.0000

4

0.4766

0.9511

0.9990

0.9060

0.6800

0.4712

0.9545

1.0000

5

0.4619

0.9750

1.0000

0.9516

0.6800

0.4608

0.9447

1.0000

6

0.4499

0.9839

0.9990

0.9696

0.6800

0.4433

0.9946

1.0000

7

0.4384

0.9878

1.0000

0.9763

0.6800

0.4333

0.9936

1.0000

8

0.4287

0.9936

1.0000

0.9877

0.6800

0.4243

0.9951

1.0000

9

0.4207

0.9946

0.9962

0.9934

0.6800

0.4186

0.9892

1.0000

10

0.4136

0.9912

0.9971

0.9858

0.6800

0.4114

0.9946

1.0000

Epoch

Val. recall

Val. F1- score

Learning rate

     

1

0.0969

0.6800

0.0010

     

2

0.6325

0.6800

0.0010

     

3

0.8139

0.6800

0.0010

     

4

0.9117

0.6800

0.0010

     

5

0.8927

0.6800

0.0010

     

6

0.9896

0.6800

0.00090484

     

7

0.9877

0.6800

0.00081873

     

8

0.9905

0.6800

0.00074082

     

9

0.9791

0.6800

Learning rate

     

10

0.9896

Val. F1- score

0.0010

     
Back to article page