---
library_name: transformers
license: other
base_model: apple/mobilevit-x-small
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: mobilevit-x-small
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.995850622406639
---

<!-- 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. -->

# mobilevit-x-small

This model is a fine-tuned version of [apple/mobilevit-x-small](https://huggingface.co/apple/mobilevit-x-small) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0196
- Accuracy: 0.9959

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6911        | 1.0   | 34   | 0.6932          | 0.5083   |
| 0.6584        | 2.0   | 68   | 0.6287          | 0.7510   |
| 0.5388        | 3.0   | 102  | 0.4852          | 0.8734   |
| 0.3891        | 4.0   | 136  | 0.3065          | 0.9357   |
| 0.2915        | 5.0   | 170  | 0.2005          | 0.9647   |
| 0.2319        | 6.0   | 204  | 0.1498          | 0.9689   |
| 0.2038        | 7.0   | 238  | 0.1228          | 0.9710   |
| 0.1641        | 8.0   | 272  | 0.0892          | 0.9855   |
| 0.1525        | 9.0   | 306  | 0.0778          | 0.9834   |
| 0.1584        | 10.0  | 340  | 0.0565          | 0.9896   |
| 0.1194        | 11.0  | 374  | 0.0491          | 0.9917   |
| 0.1222        | 12.0  | 408  | 0.0436          | 0.9896   |
| 0.1229        | 13.0  | 442  | 0.0360          | 0.9979   |
| 0.1334        | 14.0  | 476  | 0.0326          | 0.9959   |
| 0.122         | 15.0  | 510  | 0.0425          | 0.9896   |
| 0.096         | 16.0  | 544  | 0.0315          | 0.9959   |
| 0.0989        | 17.0  | 578  | 0.0303          | 0.9938   |
| 0.1085        | 18.0  | 612  | 0.0262          | 0.9959   |
| 0.0957        | 19.0  | 646  | 0.0232          | 0.9959   |
| 0.1129        | 20.0  | 680  | 0.0266          | 0.9959   |
| 0.0843        | 21.0  | 714  | 0.0234          | 0.9959   |
| 0.0868        | 22.0  | 748  | 0.0217          | 0.9959   |
| 0.0867        | 23.0  | 782  | 0.0233          | 0.9959   |
| 0.0947        | 24.0  | 816  | 0.0204          | 0.9959   |
| 0.0786        | 25.0  | 850  | 0.0199          | 0.9959   |
| 0.1009        | 26.0  | 884  | 0.0212          | 0.9959   |
| 0.0785        | 27.0  | 918  | 0.0204          | 0.9959   |
| 0.0811        | 28.0  | 952  | 0.0180          | 0.9959   |
| 0.0883        | 29.0  | 986  | 0.0193          | 0.9959   |
| 0.0988        | 30.0  | 1020 | 0.0196          | 0.9959   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1