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# computer_partsclassifier-model

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5569
- Accuracy: 0.8138

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.97  | 9    | 1.0514          | 0.5172   |
| 1.0783        | 1.95  | 18   | 0.9347          | 0.6828   |
| 0.9676        | 2.92  | 27   | 0.7734          | 0.7517   |
| 0.7674        | 4.0   | 37   | 0.6470          | 0.7931   |
| 0.6162        | 4.97  | 46   | 0.5806          | 0.8      |
| 0.4838        | 5.95  | 55   | 0.5836          | 0.7931   |
| 0.4034        | 6.92  | 64   | 0.5778          | 0.8      |
| 0.325         | 8.0   | 74   | 0.5584          | 0.8069   |
| 0.2824        | 8.97  | 83   | 0.4549          | 0.8207   |
| 0.2252        | 9.95  | 92   | 0.5479          | 0.8      |
| 0.2017        | 10.92 | 101  | 0.5885          | 0.7724   |
| 0.183         | 12.0  | 111  | 0.5698          | 0.8      |
| 0.1709        | 12.97 | 120  | 0.5687          | 0.8      |
| 0.1709        | 13.95 | 129  | 0.6270          | 0.7793   |
| 0.1647        | 14.92 | 138  | 0.5652          | 0.8      |
| 0.1543        | 16.0  | 148  | 0.5965          | 0.8138   |
| 0.1676        | 16.97 | 157  | 0.5710          | 0.8      |
| 0.1562        | 17.95 | 166  | 0.6193          | 0.7724   |
| 0.1402        | 18.92 | 175  | 0.6086          | 0.7862   |
| 0.1313        | 19.46 | 180  | 0.5569          | 0.8138   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

cable case cpu