README.md

---
library_name: ultralytics
tags:
  - image-classification
  - facial-expression-recognition
  - fer2013
metrics:
  - accuracy
---

# FER2013 Emotion Classification (YOLO11s Ensemble)

This repo contains an **ensemble** of two Ultralytics YOLO11s classification checkpoints.
The final prediction is computed by **averaging softmax probabilities** from both models.

## Test Results (FER2013 test set, 7,178 images)
- **Top-1 Accuracy (ensemble)**: **70.41%**

Per-class accuracy (TEST, ensemble):
- angry: 63.99%
- disgust: 61.26%
- fear: 49.51%
- happy: 89.85%
- neutral: 70.88%
- sad: 57.82%
- surprise: 81.47%

## Files
- `weights/modelA_yolo11s_96.pt`  (YOLO11s trained @96)
- `weights/modelB_yolo11s_128.pt` (YOLO11s fine-tuned @128)
- `ensemble_predict.py` (simple CLI for ensemble inference)

## Usage
```bash
pip install ultralytics numpy
python ensemble_predict.py --image path/to/image.jpg --device 0
```