Fer2013 Recognition - ResNet18 With Tricks
This work is the final project of the Computer Vision Course of USTC. However, I achieve the highest single-network classification accuracy on FER2013 based on ResNet18. To my best knowledge, this work achieves state-of-the-art single-network accuracy of 73.70 % on FER2013 without using extra training data, which exceeds the previous work [1] of 73.28%.
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Datasets
FER2013
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Facial Expression Recognition (FER) | FER2013 | ResNet18 With Tricks | Accuracy | 73.70 | # 7 |
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