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

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: 0.9705
- Accuracy: 0.7556

## 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 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|
| 1.464         | 0.5688 | 5000  | 1.4063          | 0.6493   |
| 1.2318        | 1.1377 | 10000 | 1.2154          | 0.6937   |
| 1.1699        | 1.7065 | 15000 | 1.1495          | 0.7096   |
| 1.1018        | 2.2753 | 20000 | 1.1081          | 0.7190   |
| 1.0837        | 2.8441 | 25000 | 1.0871          | 0.7240   |
| 1.0343        | 3.4130 | 30000 | 1.0550          | 0.7326   |
| 1.0198        | 3.9818 | 35000 | 1.0281          | 0.739    |
| 0.9795        | 4.5506 | 40000 | 1.0125          | 0.7435   |
| 0.9339        | 5.1195 | 45000 | 0.9964          | 0.7475   |
| 0.9292        | 5.6883 | 50000 | 0.9843          | 0.7510   |
| 0.8975        | 6.2571 | 55000 | 0.9795          | 0.7528   |
| 0.8957        | 6.8259 | 60000 | 0.9723          | 0.7548   |
| 0.8721        | 7.3948 | 65000 | 0.9716          | 0.7555   |
| 0.8725        | 7.9636 | 70000 | 0.9705          | 0.7556   |


### Framework versions

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