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
pip install ultralytics numpy
python ensemble_predict.py --image path/to/image.jpg --device 0
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# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("reskyayu/fer2013-yolo11s-ensemble") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True)