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---
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
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