---
license: other
base_model: apple/mobilevitv2-1.0-imagenet1k-256
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: quickdraw-MobileVITV2-1.0-Finetune
  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-MobileVITV2-1.0-Finetune

This model is a fine-tuned version of [apple/mobilevitv2-1.0-imagenet1k-256](https://huggingface.co/apple/mobilevitv2-1.0-imagenet1k-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0138
- Accuracy: 0.7524

## 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: 10000
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|
| 1.4934        | 0.5688 | 5000  | 1.4418          | 0.6444   |
| 1.2717        | 1.1377 | 10000 | 1.2881          | 0.6771   |
| 1.1742        | 1.7065 | 15000 | 1.1661          | 0.7052   |
| 1.0846        | 2.2753 | 20000 | 1.1149          | 0.7178   |
| 1.0619        | 2.8441 | 25000 | 1.0778          | 0.7261   |
| 1.0029        | 3.4130 | 30000 | 1.0556          | 0.7322   |
| 0.9936        | 3.9818 | 35000 | 1.0317          | 0.7375   |
| 0.9429        | 4.5506 | 40000 | 1.0150          | 0.7424   |
| 0.8818        | 5.1195 | 45000 | 1.0119          | 0.7451   |
| 0.8868        | 5.6883 | 50000 | 0.9947          | 0.7486   |
| 0.8323        | 6.2571 | 55000 | 1.0007          | 0.7491   |
| 0.838         | 6.8259 | 60000 | 0.9854          | 0.7522   |
| 0.7835        | 7.3948 | 65000 | 0.9989          | 0.7521   |
| 0.7836        | 7.9636 | 70000 | 0.9900          | 0.7535   |
| 0.7451        | 8.5324 | 75000 | 1.0044          | 0.7529   |
| 0.7207        | 9.1013 | 80000 | 1.0054          | 0.7531   |
| 0.721         | 9.6701 | 85000 | 1.0081          | 0.7529   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1
- Datasets 2.19.1
- Tokenizers 0.19.1