---
license: other
base_model: apple/mobilevit-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
model-index:
- name: test1
  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. -->

# test1

This model is a fine-tuned version of [apple/mobilevit-small](https://huggingface.co/apple/mobilevit-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1366
- Accuracy: 0.7952
- F1: 0.7855
- Recall: 0.7952

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1     | Recall |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:------:|
| No log        | 0.4554 | 500   | 0.6376          | 0.7896   | 0.7429 | 0.7896 |
| 0.657         | 0.9107 | 1000  | 0.5839          | 0.8109   | 0.7679 | 0.8109 |
| 0.657         | 1.3661 | 1500  | 0.7632          | 0.7322   | 0.7195 | 0.7322 |
| 0.5653        | 1.8215 | 2000  | 0.5927          | 0.8144   | 0.7689 | 0.8144 |
| 0.5653        | 2.2769 | 2500  | 0.5855          | 0.8174   | 0.7765 | 0.8174 |
| 0.5128        | 2.7322 | 3000  | 0.5567          | 0.8210   | 0.7931 | 0.8210 |
| 0.5128        | 3.1876 | 3500  | 0.5578          | 0.8214   | 0.7894 | 0.8214 |
| 0.4648        | 3.6430 | 4000  | 0.5699          | 0.8236   | 0.7928 | 0.8236 |
| 0.4648        | 4.0984 | 4500  | 0.6039          | 0.8053   | 0.7850 | 0.8053 |
| 0.411         | 4.5537 | 5000  | 0.5662          | 0.8203   | 0.7989 | 0.8203 |
| 0.411         | 5.0091 | 5500  | 0.6043          | 0.8252   | 0.7962 | 0.8252 |
| 0.3532        | 5.4645 | 6000  | 0.6559          | 0.8060   | 0.7915 | 0.8060 |
| 0.3532        | 5.9199 | 6500  | 0.6310          | 0.8175   | 0.7919 | 0.8175 |
| 0.2847        | 6.3752 | 7000  | 0.7075          | 0.8029   | 0.7890 | 0.8029 |
| 0.2847        | 6.8306 | 7500  | 0.8056          | 0.7743   | 0.7745 | 0.7743 |
| 0.2265        | 7.2860 | 8000  | 0.8991          | 0.7957   | 0.7875 | 0.7957 |
| 0.2265        | 7.7413 | 8500  | 0.8929          | 0.7904   | 0.7866 | 0.7904 |
| 0.1626        | 8.1967 | 9000  | 0.9503          | 0.8022   | 0.7883 | 0.8022 |
| 0.1626        | 8.6521 | 9500  | 1.0467          | 0.7904   | 0.7838 | 0.7904 |
| 0.1099        | 9.1075 | 10000 | 1.0435          | 0.8009   | 0.7877 | 0.8009 |
| 0.1099        | 9.5628 | 10500 | 1.1366          | 0.7952   | 0.7855 | 0.7952 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.2.1
- Datasets 2.19.1
- Tokenizers 0.19.1