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Hassan48khan/SavorNet-Adaptive-Attention-Fusion-for-Ethiopian-Cuisine-Classification

Type de record:

model
Créateur:
Has
Hôte:
SavorNet: Adaptive Attention Fusion for Ethiopian Cuisine Classification Overview SavorNet is a deep learning model designed to classify traditional Ethiopian dishes, addressing the underrepresentation of Ethiopian cuisine in existing food recognition systems. # SavorNet-Adaptive-Attention-Fusion-for-Ethiopian-Cuisine-Classification SavorNet: Adaptive Attention Fusion for Ethiopian Cuisine Classification Overview SavorNet is a deep learning model designed to classify traditional Ethiopian dishes, addressing the underrepresentation of Ethiopian cuisine in existing food recognition systems. # Key Features Uses adaptive attention fusion to dynamically combine features from DenseNet121 and ResNet50V2 backbones. Outperforms traditional ensemble methods and individual models. Achieved 92.2% test accuracy and 96.1% top-2 accuracy on a curated Ethiopian food dataset. # Performance Comparison Model Test Accuracy SavorNet 92.2% Conventional Ensemble 88.31% ResNet50V2 86.36% DenseNet121 83.12% # Applications Automated food processing and quality control Dietary analysis Cultural preservation of underrepresented cuisines # You can download ipynb file and access the whole pipeline

Visit

github.com

Tasks

computer visionimage classification

Languages

Amharic

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