transformers tensorboard safetensors mobilevitv2 image-classification generated_from_trainer dataset:food101 base_model:apple/mobilevitv2-1.0-imagenet1k-256 license:other model-index autotrain_compatible endpoints_compatible region:us
---
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
---

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# 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
