| + Checkpoint Averaging                          | 74.028 | 91.382 |
| + Label Smoothing & Stochastic Depth & LR noise | 75.536 | 92.368 |
Note that once we’ve achieved an acceptable accuracy, we verified the model performance on the hold-out test dataset which hasn't been used before for training or hyper-parameter tuning. This process helps us detect overfitting and is always performed for all pre-trained models prior their release.
Quantization
We currently offer quantized weights for the QNNPACK backend of the MobileNetV3-Large variant which provides a speed-up of 2.5x. To quantize the model, Quantized Aware Training (QAT) was used. The hyper parameters and the scripts used to train the model can be found in our references folder.