File size: 1,999 Bytes
ad803c0 edf6a86 ad803c0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | # 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
```bash
pip install -e .
```
## Usage
```bash
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
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license: apache-2.0
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