| | --- |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: MobileNetV2-KD-VGGFace |
| | results: [] |
| | license: mit |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # MobileNetV2-KD-VGGFace |
| |
|
| | This model is trained via KD from [ViT](https://huggingface.co/skutaada/VIT-VGGFace) on first 50k samples of VGGFace dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.4919 |
| | - Accuracy: 0.7836 |
| |
|
| | ## Model description |
| |
|
| | More information needed |
| |
|
| | ## Intended uses & limitations |
| |
|
| | More information needed |
| |
|
| | ## Training and evaluation data |
| |
|
| | More information needed |
| |
|
| | ## Training procedure |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 5e-05 |
| | - train_batch_size: 24 |
| | - eval_batch_size: 24 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 20 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| | |:-------------:|:-----:|:-----:|:---------------:|:--------:| |
| | | 2.7506 | 1.0 | 1667 | 2.2449 | 0.0726 | |
| | | 2.105 | 2.0 | 3334 | 1.6904 | 0.2493 | |
| | | 1.6544 | 3.0 | 5001 | 1.3206 | 0.4043 | |
| | | 1.3357 | 4.0 | 6668 | 1.0675 | 0.5078 | |
| | | 1.1104 | 5.0 | 8335 | 0.9302 | 0.5582 | |
| | | 0.9287 | 6.0 | 10002 | 0.8738 | 0.5972 | |
| | | 0.7899 | 7.0 | 11669 | 0.7972 | 0.6388 | |
| | | 0.6738 | 8.0 | 13336 | 0.7074 | 0.6822 | |
| | | 0.5803 | 9.0 | 15003 | 0.6630 | 0.7009 | |
| | | 0.5038 | 10.0 | 16670 | 0.5855 | 0.735 | |
| | | 0.4366 | 11.0 | 18337 | 0.5761 | 0.7415 | |
| | | 0.3762 | 12.0 | 20004 | 0.5642 | 0.7496 | |
| | | 0.3321 | 13.0 | 21671 | 0.5373 | 0.7652 | |
| | | 0.2916 | 14.0 | 23338 | 0.5314 | 0.7625 | |
| | | 0.2615 | 15.0 | 25005 | 0.6206 | 0.7281 | |
| | | 0.2357 | 16.0 | 26672 | 0.5437 | 0.763 | |
| | | 0.2153 | 17.0 | 28339 | 0.5335 | 0.763 | |
| | | 0.1986 | 18.0 | 30006 | 0.4892 | 0.7869 | |
| | | 0.1866 | 19.0 | 31673 | 0.5368 | 0.7645 | |
| | | 0.1765 | 20.0 | 33340 | 0.4919 | 0.7836 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.42.4 |
| | - Pytorch 2.3.1+rocm6.0 |
| | - Datasets 2.20.0 |
| | - Tokenizers 0.19.1 |
