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---
license: other
base_model: apple/mobilevitv2-1.0-imagenet1k-256
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: mobilevit-finetuned-food101
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: train[:5000]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.874
---
<!-- 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. -->
# mobilevit-finetuned-food101
This model is a fine-tuned version of [apple/mobilevitv2-1.0-imagenet1k-256](https://huggingface.co/apple/mobilevitv2-1.0-imagenet1k-256) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4191
- Accuracy: 0.874
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9487 | 0.98 | 23 | 1.9476 | 0.151 |
| 1.9273 | 2.0 | 47 | 1.9070 | 0.24 |
| 1.8561 | 2.98 | 70 | 1.8401 | 0.448 |
| 1.7788 | 4.0 | 94 | 1.7301 | 0.612 |
| 1.6586 | 4.98 | 117 | 1.5863 | 0.676 |
| 1.4603 | 6.0 | 141 | 1.4199 | 0.72 |
| 1.3027 | 6.98 | 164 | 1.2215 | 0.734 |
| 1.1717 | 8.0 | 188 | 1.0581 | 0.759 |
| 0.9601 | 8.98 | 211 | 0.9013 | 0.769 |
| 0.8482 | 10.0 | 235 | 0.7866 | 0.791 |
| 0.7276 | 10.98 | 258 | 0.7112 | 0.803 |
| 0.6449 | 12.0 | 282 | 0.6132 | 0.835 |
| 0.6279 | 12.98 | 305 | 0.6069 | 0.83 |
| 0.5982 | 14.0 | 329 | 0.5637 | 0.832 |
| 0.5766 | 14.98 | 352 | 0.5149 | 0.857 |
| 0.5345 | 16.0 | 376 | 0.5392 | 0.837 |
| 0.494 | 16.98 | 399 | 0.5017 | 0.848 |
| 0.4953 | 18.0 | 423 | 0.5002 | 0.846 |
| 0.5118 | 18.98 | 446 | 0.4782 | 0.856 |
| 0.4708 | 20.0 | 470 | 0.4898 | 0.858 |
| 0.4774 | 20.98 | 493 | 0.4769 | 0.851 |
| 0.4848 | 22.0 | 517 | 0.4665 | 0.841 |
| 0.4533 | 22.98 | 540 | 0.4890 | 0.837 |
| 0.4449 | 24.0 | 564 | 0.4558 | 0.857 |
| 0.4205 | 24.98 | 587 | 0.4767 | 0.857 |
| 0.4417 | 26.0 | 611 | 0.4476 | 0.853 |
| 0.4333 | 26.98 | 634 | 0.4853 | 0.834 |
| 0.4545 | 28.0 | 658 | 0.4573 | 0.847 |
| 0.4489 | 28.98 | 681 | 0.4659 | 0.845 |
| 0.4172 | 29.36 | 690 | 0.4191 | 0.874 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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