Open Access
Table 4
Performance comparison in terms of average F1 score and the average of the accuracy of five folds of cross-validation.
| Model | Bands | Type of data | Accuracy | F1 score | Num params |
|---|---|---|---|---|---|
| ConvEntion (Ours) | ugriz | Images | 79.83 | 70.62 | 1.253M |
| CNN+GRU (Gómez et al. 2020) | ugriz | Images | 66.39 | 63.22 | 1.993M |
| ConvEntion (Ours) | g | Images | 76.89 | 63.20 | 1.253M |
| CNN+GRU (Gómez et al. 2020) | g | Images | 63.67 | 61.00 | 1.992M |
| CNN+LSTM (Carrasco-Davis et al. 2019) | ugriz | Images | 64.08 | 60.65 | 2.190M |
| CNN+LSTM (Carrasco-Davis et al. 2019) | g | Images | 63.00 | 60.00 | 2.189M |
| SuperNNova (Bayes) (Möller & de Boissière 2020) | ugriz | Light curves | 65.54 | 55.40 | – |
| SITS-BERT (Yuan & Lin 2021) | ugriz | Light curves | 67.43 | 51.60 | 0.596M |
| SCONE (CNN) (Qu et al. 2021) | ugriz | Light curves | 62.57 | 50.43 | 22.2K |
| SuperNNova (RNN) (Möller & de Boissière 2020) | ugriz | Light curves | 56.30 | 42.60 | – |
| LSTM | ugriz | Light curves | 55.24 | 40.33 | 60K |
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