![]() Its effect is smaller than random variance. Here we use RandErase following DeiT: -reprob 0.25. Turning off amp could solve this issue, but is slower. In this repo, we use -global_pool for fine-tuning using -cls_token performs similarly, but there is a chance of producing NaN when fine-tuning ViT-Huge in GPUs. We have observed different numerical behavior between the two platforms. This re-implementation is in PyTorch GPU with automatic mixed precision ( ). The original MAE implementation was in TensorFlow TPU with no explicit mixed precision. The fine-tuning hyper-parameters are slightly different from the default baseline using unnormalized pixels. Finetune uses AcoustID and Chromaprint technologies to listen to your music files, generate a fingerprint and search for matching songs in a database. On OS X, Finetune also takes care of updating your iTunes Library. The pre-trained models we provide are trained with normalized pixels -norm_pix_loss (1600 epochs, Table 3 in paper). Finetune can fix or add song information, cover art, lyrics and remove duplicate and missing tracks from your library. ![]()
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