Utilizing Machine Learning techniques to identify sick bees in a cost-effective manner
Using a ResNet50-based architecture, we performed transfer learning on a frozen ImageNet-trained model for 100 epochs and selected the best checkpoint based on validation metrics (#89).
Accuracy |
Loss |
|---|---|
Precision |
Recall |
Epoch 89/100
loss: 0.0032 - binary_accuracy: 0.9994 - precision_1: 0.9997 - recall_1: 0.9993
val_loss: 0.8938 - val_binary_accuracy: 0.8494 - val_precision_1: 0.8325 - val_recall_1: 0.9578Validation Accuracy: 84.94%
Validation Precision: 83.25%
Validation Recall: 95.78%