---
license: other
base_model: apple/mobilevit-xx-small
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mnist-mobilevit
  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. -->

# mnist-mobilevit

This model is a fine-tuned version of [apple/mobilevit-xx-small](https://huggingface.co/apple/mobilevit-xx-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0185
- Accuracy: 0.9929

## 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: 0.0008
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.026         | 0.2132 | 100  | 0.0458          | 0.9867   |
| 0.0232        | 0.4264 | 200  | 0.0453          | 0.9867   |
| 0.0277        | 0.6397 | 300  | 0.0484          | 0.9863   |
| 0.0293        | 0.8529 | 400  | 0.0469          | 0.9865   |
| 0.0235        | 1.0661 | 500  | 0.0288          | 0.9899   |
| 0.0203        | 1.2793 | 600  | 0.0253          | 0.9924   |
| 0.0182        | 1.4925 | 700  | 0.0286          | 0.9916   |
| 0.0205        | 1.7058 | 800  | 0.0203          | 0.9935   |
| 0.0162        | 1.9190 | 900  | 0.0238          | 0.9913   |
| 0.0118        | 2.1322 | 1000 | 0.0247          | 0.9916   |
| 0.0121        | 2.3454 | 1100 | 0.0194          | 0.9932   |
| 0.0154        | 2.5586 | 1200 | 0.0194          | 0.9933   |
| 0.015         | 2.7719 | 1300 | 0.0216          | 0.9933   |
| 0.0145        | 2.9851 | 1400 | 0.0238          | 0.9919   |
| 0.0098        | 3.1983 | 1500 | 0.0208          | 0.993    |
| 0.0093        | 3.4115 | 1600 | 0.0218          | 0.9929   |
| 0.0073        | 3.6247 | 1700 | 0.0189          | 0.9933   |
| 0.008         | 3.8380 | 1800 | 0.0194          | 0.9932   |
| 0.006         | 4.0512 | 1900 | 0.0183          | 0.9938   |
| 0.0063        | 4.2644 | 2000 | 0.0184          | 0.9934   |
| 0.0043        | 4.4776 | 2100 | 0.0184          | 0.9932   |
| 0.0035        | 4.6908 | 2200 | 0.0183          | 0.9931   |
| 0.0061        | 4.9041 | 2300 | 0.0184          | 0.9931   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1