Skip to main content

Table 2 mAP for the validation and test sets for different model architectures

From: Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops

  ResNet50 ResNet101 Inception v2 Inception-ResNet v2
  val test val test val test val test
Pennisetum glaucum 85.80 93.66 87.62 93.66 86.16 93.06 88.91 94.25
Secale cereale 89.99 91.83 91.58 92.70 90.07 91.48 92.67 94.21
Zea mays 96.21 96.29 96.54 96.69 95.81 95.62 97.48 97.90
  1. Results (in %) on the validation set are indicated by val and results on the test set are indicated by test. Italic values indicate the model with the highest mAP for each seed type
\