Release v1.0.0
Date: September 19, 2026
What's New
Initial release of Dog Emotion Detector — a mobile-first classifier for detecting dog emotions from images.
Features
- 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
Performance
| Metric | Value |
|---|---|
| Accuracy | 69.9% |
| Macro F1 | 69.2% |
| Model size | 24 MB |
| Inference latency | ~22ms (CPU) |
Per-class Results
| Class | F1 Score |
|---|---|
| happy | 0.89 |
| relax | 0.79 |
| frown | 0.61 |
| angry | 0.53 |
| alert | 0.58 |
Installation
pip install -e .
Usage
python main.py train --config configs/config.yaml
Model Architecture
- 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)
Dataset
- Total images: 9,325
- Classes: 5 (alert, angry, frown, happy, relax)
- Images per class: ~1,865
- Split: 80% train / 10% validation / 10% test
Known Limitations
- 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)
Future Improvements
- CutMix/Mixup augmentation for better generalization
- Test-time augmentation for inference boost
- Ensemble methods for production deployment
- Larger training dataset (50K+ images)
License
MIT