The model takes in (50, 84) image, resizes it to (224, 224), applies mean/std normalization, 2d backbone and arcface head.
To train a model:
python3 train.py --config config_1.py,
NB: takes ~10 hours to saturate validation metric on 4090 GPU assuming there is no data loading bottleneck (almost any CPU would do)
| model | approx score LB |
|---|---|
| from scratch, patch transformer | 0.30 |
| whisper-base, take encoder, downsample pos encoding | 0.35 |
| 2d cnns, e.g. convnext_tiny | 0.45 |
| add resize from orig resolution to (224, 224) | 0.55 |
| transformers, e.g. maxvit (works best) | 0.64 |
| +5 models ensemble | 0.665 |
| +query expansion | 0.680 |
