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Integration:  dvc git github
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README.md

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See this repository on DAGsHub and GitHub

BEE Healthy!

AI-Powered Assessment of Bee Colony Health

Utilizing Machine Learning techniques to identify sick bees in a cost-effective manner

Model Training

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

Download Model

Validation performance

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%

Tip!

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About

BEE Healthy! AI-Powered Assessment of Bee Colony Health

https://github.com/arjvik/BEEHealthy
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