---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-cards-base-classifier-defects-finder
  results: []
---

<!-- 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-base-patch16-224-in21k-cards-base-classifier-defects-finder

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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0683
- Accuracy: 0.999

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.4892        | 0.9929 | 70   | 1.3366          | 0.859    |
| 0.4362        | 2.0    | 141  | 0.4142          | 0.971    |
| 0.231         | 2.9929 | 211  | 0.2250          | 0.988    |
| 0.1654        | 4.0    | 282  | 0.1687          | 0.982    |
| 0.1289        | 4.9929 | 352  | 0.1322          | 0.991    |
| 0.0999        | 6.0    | 423  | 0.1184          | 0.988    |
| 0.0824        | 6.9929 | 493  | 0.0852          | 0.996    |
| 0.0789        | 8.0    | 564  | 0.0809          | 0.998    |
| 0.07          | 8.9929 | 634  | 0.0723          | 0.997    |
| 0.067         | 9.9291 | 700  | 0.0683          | 0.999    |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1