---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: MobileNetV2-KD-VGGFace
  results: []
---

<!-- 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 a fine-tuned version of [](https://huggingface.co/) on an unknown 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