Are you sure you want to delete this access key?
Legend |
---|
DVC Managed File |
Git Managed File |
Metric |
Stage File |
External File |
Legend |
---|
DVC Managed File |
Git Managed File |
Metric |
Stage File |
External File |
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: 0.9578
Validation Accuracy: 84.94%Validation Precision: 83.25%Validation Recall: 95.78%
Press p or to see the previous file or, n or to see the next file
BEE Healthy! AI-Powered Assessment of Bee Colony Health
https://github.com/arjvik/BEEHealthyAre you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?