---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: computer_parts_classifier-model
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train[:722]
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8068965517241379
---

<!-- 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. -->

# 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: 0.5140
- Accuracy: 0.8069

## 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    | 1.0689          | 0.5379   |
| 1.1042        | 1.95  | 18   | 0.9123          | 0.6897   |
| 0.9605        | 2.92  | 27   | 0.7676          | 0.7379   |
| 0.7855        | 4.0   | 37   | 0.6722          | 0.7586   |
| 0.626         | 4.97  | 46   | 0.5915          | 0.8069   |
| 0.5102        | 5.95  | 55   | 0.5672          | 0.8138   |
| 0.4266        | 6.92  | 64   | 0.5106          | 0.8483   |
| 0.3561        | 8.0   | 74   | 0.5587          | 0.8138   |
| 0.3126        | 8.97  | 83   | 0.5492          | 0.8069   |
| 0.294         | 9.95  | 92   | 0.5589          | 0.7862   |
| 0.2287        | 10.92 | 101  | 0.5579          | 0.8069   |
| 0.2282        | 12.0  | 111  | 0.5193          | 0.8138   |
| 0.2261        | 12.97 | 120  | 0.4383          | 0.8552   |
| 0.2261        | 13.95 | 129  | 0.5205          | 0.7931   |
| 0.1996        | 14.92 | 138  | 0.5037          | 0.8138   |
| 0.1796        | 16.0  | 148  | 0.4986          | 0.8138   |
| 0.1583        | 16.97 | 157  | 0.5583          | 0.7931   |
| 0.1692        | 17.95 | 166  | 0.4743          | 0.8276   |
| 0.1577        | 18.92 | 175  | 0.4867          | 0.8345   |
| 0.1706        | 19.46 | 180  | 0.5140          | 0.8069   |


### Framework versions

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