---
license: other
base_model: apple/mobilevit-small
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- f1
- accuracy
model-index:
- name: car_identified_model_11
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: F1
      type: f1
      value: 0.7241379310344829
    - name: Accuracy
      type: accuracy
      value: 0.08333333333333333
---

<!-- 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. -->

# car_identified_model_11

This model is a fine-tuned version of [apple/mobilevit-small](https://huggingface.co./apple/mobilevit-small) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6686
- F1: 0.7241
- Roc Auc: 0.6667
- Accuracy: 0.0833

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.2582        | 1.0   | 1    | 0.6938          | 0.5926 | 0.5417  | 0.0833   |
| 0.2582        | 2.0   | 2    | 0.6937          | 0.6415 | 0.6042  | 0.0833   |
| 0.2582        | 3.0   | 4    | 0.6918          | 0.6429 | 0.5833  | 0.0      |
| 0.2582        | 4.0   | 5    | 0.6893          | 0.6316 | 0.5625  | 0.0      |
| 0.2582        | 5.0   | 6    | 0.6871          | 0.6667 | 0.6042  | 0.0833   |
| 0.2582        | 6.0   | 8    | 0.6844          | 0.6786 | 0.625   | 0.0833   |
| 0.2582        | 7.0   | 9    | 0.6827          | 0.7018 | 0.6458  | 0.0833   |
| 0.2582        | 8.0   | 10   | 0.6817          | 0.6667 | 0.6042  | 0.0833   |
| 0.2582        | 9.0   | 11   | 0.6809          | 0.6897 | 0.625   | 0.0833   |
| 0.2582        | 10.0  | 12   | 0.6804          | 0.6897 | 0.625   | 0.0833   |
| 0.2582        | 11.0  | 14   | 0.6792          | 0.6897 | 0.625   | 0.0833   |
| 0.2582        | 12.0  | 15   | 0.6787          | 0.7119 | 0.6458  | 0.0833   |
| 0.2582        | 13.0  | 16   | 0.6780          | 0.7119 | 0.6458  | 0.0833   |
| 0.2582        | 14.0  | 18   | 0.6771          | 0.7241 | 0.6667  | 0.0833   |
| 0.2582        | 15.0  | 19   | 0.6765          | 0.7241 | 0.6667  | 0.0833   |
| 0.2582        | 16.0  | 20   | 0.6762          | 0.7458 | 0.6875  | 0.0833   |
| 0.2582        | 17.0  | 21   | 0.6758          | 0.7333 | 0.6667  | 0.0833   |
| 0.2582        | 18.0  | 22   | 0.6753          | 0.7458 | 0.6875  | 0.0833   |
| 0.2582        | 19.0  | 24   | 0.6744          | 0.7333 | 0.6667  | 0.0833   |
| 0.2582        | 20.0  | 25   | 0.6740          | 0.7241 | 0.6667  | 0.0833   |
| 0.2582        | 21.0  | 26   | 0.6737          | 0.7241 | 0.6667  | 0.0833   |
| 0.2582        | 22.0  | 28   | 0.6733          | 0.7458 | 0.6875  | 0.0833   |
| 0.2582        | 23.0  | 29   | 0.6725          | 0.7458 | 0.6875  | 0.0833   |
| 0.2582        | 24.0  | 30   | 0.6720          | 0.7368 | 0.6875  | 0.0833   |
| 0.2582        | 25.0  | 31   | 0.6719          | 0.7241 | 0.6667  | 0.0833   |
| 0.2582        | 26.0  | 32   | 0.6713          | 0.7241 | 0.6667  | 0.0833   |
| 0.2582        | 27.0  | 34   | 0.6711          | 0.7241 | 0.6667  | 0.0833   |
| 0.2582        | 28.0  | 35   | 0.6705          | 0.7241 | 0.6667  | 0.0833   |
| 0.2582        | 29.0  | 36   | 0.6700          | 0.7368 | 0.6875  | 0.0833   |
| 0.2582        | 30.0  | 38   | 0.6696          | 0.7241 | 0.6667  | 0.0833   |
| 0.2582        | 31.0  | 39   | 0.6695          | 0.7241 | 0.6667  | 0.0833   |
| 0.2582        | 32.0  | 40   | 0.6693          | 0.7368 | 0.6875  | 0.1667   |
| 0.2582        | 33.0  | 41   | 0.6692          | 0.7241 | 0.6667  | 0.0833   |
| 0.2582        | 34.0  | 42   | 0.6694          | 0.7241 | 0.6667  | 0.0833   |
| 0.2582        | 35.0  | 44   | 0.6692          | 0.7241 | 0.6667  | 0.0833   |
| 0.2582        | 36.0  | 45   | 0.6693          | 0.7241 | 0.6667  | 0.0833   |
| 0.2582        | 37.0  | 46   | 0.6693          | 0.7241 | 0.6667  | 0.0833   |
| 0.2582        | 38.0  | 48   | 0.6690          | 0.7241 | 0.6667  | 0.0833   |
| 0.2582        | 39.0  | 49   | 0.6689          | 0.7368 | 0.6875  | 0.0833   |
| 0.2582        | 40.0  | 50   | 0.6686          | 0.7241 | 0.6667  | 0.0833   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1