---
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-mobilenet-beans-224
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: beans
      type: beans
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7265625
---

<!-- 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. -->

# ViT distilled to MobileNet

This model is a distilled model, where teacher model is [merve/beans-vit-224](https://huggingface.co./merve/beans-vit-224), fine-tuned [google/vit-base-patch16-224-in21k](https://huggingface.co./google/vit-base-patch16-224-in21k) on the beans dataset.
Student model is randomly initialized MobileNetV2.
It achieves the following results on the evaluation set:
- Loss: 0.5922
- Accuracy: 0.7266


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9217        | 1.0   | 130  | 1.0079          | 0.3835   |
| 0.8973        | 2.0   | 260  | 0.8349          | 0.4286   |
| 0.7912        | 3.0   | 390  | 0.8905          | 0.5414   |
| 0.7151        | 4.0   | 520  | 1.1400          | 0.4887   |
| 0.6797        | 5.0   | 650  | 4.5343          | 0.4135   |
| 0.6471        | 6.0   | 780  | 2.1551          | 0.3985   |
| 0.5989        | 7.0   | 910  | 0.8552          | 0.6090   |
| 0.6252        | 8.0   | 1040 | 1.7453          | 0.5489   |
| 0.6025        | 9.0   | 1170 | 0.7852          | 0.6466   |
| 0.5643        | 10.0  | 1300 | 1.4728          | 0.6090   |
| 0.5505        | 11.0  | 1430 | 1.1570          | 0.6015   |
| 0.5207        | 12.0  | 1560 | 3.2526          | 0.4436   |
| 0.4957        | 13.0  | 1690 | 0.6617          | 0.6541   |
| 0.4935        | 14.0  | 1820 | 0.7502          | 0.6241   |
| 0.4836        | 15.0  | 1950 | 1.2039          | 0.5338   |
| 0.4648        | 16.0  | 2080 | 1.0283          | 0.5338   |
| 0.4662        | 17.0  | 2210 | 0.6695          | 0.7293   |
| 0.4351        | 18.0  | 2340 | 0.8694          | 0.5940   |
| 0.4286        | 19.0  | 2470 | 1.2751          | 0.4737   |
| 0.4166        | 20.0  | 2600 | 0.8719          | 0.6241   |
| 0.4263        | 21.0  | 2730 | 0.8767          | 0.6015   |
| 0.4261        | 22.0  | 2860 | 1.2780          | 0.5564   |
| 0.4124        | 23.0  | 2990 | 1.4095          | 0.5940   |
| 0.4082        | 24.0  | 3120 | 0.9104          | 0.6015   |
| 0.3923        | 25.0  | 3250 | 0.6430          | 0.7068   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1