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# computer_parts_classifier-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: 0.5117
- 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    | 1.0525          | 0.5517   |
| 1.0645        | 1.95  | 18   | 0.9405          | 0.6      |
| 0.9405        | 2.92  | 27   | 0.7902          | 0.7034   |
| 0.7669        | 4.0   | 37   | 0.6923          | 0.7379   |
| 0.6008        | 4.97  | 46   | 0.6152          | 0.7862   |
| 0.5142        | 5.95  | 55   | 0.5639          | 0.7931   |
| 0.394         | 6.92  | 64   | 0.5640          | 0.8      |
| 0.3649        | 8.0   | 74   | 0.5181          | 0.7862   |
| 0.279         | 8.97  | 83   | 0.5094          | 0.8345   |
| 0.2549        | 9.95  | 92   | 0.4882          | 0.8276   |
| 0.1925        | 10.92 | 101  | 0.5041          | 0.8      |
| 0.2185        | 12.0  | 111  | 0.5195          | 0.8138   |
| 0.1921        | 12.97 | 120  | 0.5170          | 0.8      |
| 0.1921        | 13.95 | 129  | 0.5846          | 0.7793   |
| 0.15          | 14.92 | 138  | 0.5217          | 0.8207   |
| 0.1798        | 16.0  | 148  | 0.5421          | 0.7862   |
| 0.1729        | 16.97 | 157  | 0.5516          | 0.8207   |
| 0.1459        | 17.95 | 166  | 0.5438          | 0.7931   |
| 0.1701        | 18.92 | 175  | 0.5043          | 0.8345   |
| 0.1487        | 19.46 | 180  | 0.5117          | 0.8138   |


### Framework versions

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

cable case cpu