| # Release v1.0.0 |
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| **Date:** September 19, 2026 |
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| ## What's New |
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| Initial release of Dog Emotion Detector — a mobile-first classifier for detecting dog emotions from images. |
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| ### Features |
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| - **5-class emotion classification:** alert, angry, frown, happy, relax |
| - **Transfer learning:** EfficientNetV2B0 backbone with ImageNet weights |
| - **TFLite export:** 24 MB model with ~22ms CPU inference |
| - **Full pipeline:** data loading → augmentation → training → evaluation → export |
| - **Configurable:** YAML-based configuration for model, training, and export |
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| ### Performance |
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| | Metric | Value | |
| |--------|-------| |
| | Accuracy | 69.9% | |
| | Macro F1 | 69.2% | |
| | Model size | 24 MB | |
| | Inference latency | ~22ms (CPU) | |
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| ### Per-class Results |
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| | Class | F1 Score | |
| |-------|----------| |
| | happy | 0.89 | |
| | relax | 0.79 | |
| | frown | 0.61 | |
| | angry | 0.53 | |
| | alert | 0.58 | |
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| ## Installation |
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| ```bash |
| pip install -e . |
| ``` |
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| ## Usage |
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| ```bash |
| python main.py train --config configs/config.yaml |
| ``` |
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| ## Model Architecture |
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| - Backbone: EfficientNetV2B0 (ImageNet pretrained) |
| - Head: GlobalAveragePooling2D → Dense(256, relu) → Dropout(0.5) → Dense(5, softmax) |
| - Optimizer: Adam (lr=0.0003) |
| - Loss: Categorical crossentropy with label smoothing (0.1) |
| - Regularization: Early stopping (patience=20), reduce-LR-on-plateau (patience=8, factor=0.5) |
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| ## Dataset |
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| - Total images: 9,325 |
| - Classes: 5 (alert, angry, frown, happy, relax) |
| - Images per class: ~1,865 |
| - Split: 80% train / 10% validation / 10% test |
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| ## Known Limitations |
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| - Angry/alert/frown classes have lower F1 due to subjective visual similarity |
| - Performance is limited by dataset size (~1.8K images per class) |
| - Further improvement requires more data or advanced augmentation (CutMix/Mixup) |
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| ## Future Improvements |
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| - CutMix/Mixup augmentation for better generalization |
| - Test-time augmentation for inference boost |
| - Ensemble methods for production deployment |
| - Larger training dataset (50K+ images) |
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| ## License |
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| MIT |
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| --- |
| license: apache-2.0 |
| --- |
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