---
license: other
base_model: apple/mobilevit-xx-small
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: quickdraw-MobileViT-xxs-a
  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. -->

# quickdraw-MobileViT-xxs-a

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: 1.1512
- Accuracy: 0.7126

## 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: 512
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5000
- num_epochs: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|
| 1.8007        | 0.5688 | 5000  | 1.7490          | 0.5725   |
| 1.5116        | 1.1377 | 10000 | 1.5185          | 0.6256   |
| 1.4298        | 1.7065 | 15000 | 1.4384          | 0.6438   |
| 1.3622        | 2.2753 | 20000 | 1.3908          | 0.6547   |
| 1.332         | 2.8441 | 25000 | 1.3210          | 0.6712   |
| 1.2903        | 3.4130 | 30000 | 1.2758          | 0.6824   |
| 1.2693        | 3.9818 | 35000 | 1.2592          | 0.6864   |
| 1.2391        | 4.5506 | 40000 | 1.2169          | 0.6965   |
| 1.2078        | 5.1195 | 45000 | 1.1928          | 0.7023   |
| 1.1959        | 5.6883 | 50000 | 1.1779          | 0.7059   |
| 1.1749        | 6.2571 | 55000 | 1.1669          | 0.7083   |
| 1.1713        | 6.8259 | 60000 | 1.1564          | 0.7110   |
| 1.1573        | 7.3948 | 65000 | 1.1524          | 0.7123   |
| 1.1555        | 7.9636 | 70000 | 1.1512          | 0.7126   |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.2.1
- Datasets 2.19.1
- Tokenizers 0.19.1