---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-weldclassifyv2
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8633093525179856
---

<!-- 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-weldclassifyv2

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: 0.4613
- Accuracy: 0.8633

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 13
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.035         | 0.6410  | 100  | 1.1332          | 0.4029   |
| 0.6893        | 1.2821  | 200  | 0.7341          | 0.6655   |
| 0.5618        | 1.9231  | 300  | 0.5596          | 0.7554   |
| 0.4344        | 2.5641  | 400  | 0.5951          | 0.7770   |
| 0.1591        | 3.2051  | 500  | 0.4667          | 0.8453   |
| 0.1821        | 3.8462  | 600  | 0.5082          | 0.8345   |
| 0.0811        | 4.4872  | 700  | 0.4613          | 0.8633   |
| 0.1729        | 5.1282  | 800  | 0.6382          | 0.7986   |
| 0.1174        | 5.7692  | 900  | 0.4974          | 0.8669   |
| 0.0389        | 6.4103  | 1000 | 0.6049          | 0.8453   |
| 0.0099        | 7.0513  | 1100 | 0.6147          | 0.8561   |
| 0.0342        | 7.6923  | 1200 | 0.5603          | 0.8741   |
| 0.0175        | 8.3333  | 1300 | 0.5679          | 0.8849   |
| 0.0177        | 8.9744  | 1400 | 0.6592          | 0.8669   |
| 0.0025        | 9.6154  | 1500 | 0.6000          | 0.8669   |
| 0.0021        | 10.2564 | 1600 | 0.6060          | 0.8597   |
| 0.002         | 10.8974 | 1700 | 0.6113          | 0.8597   |
| 0.0019        | 11.5385 | 1800 | 0.6178          | 0.8561   |
| 0.0019        | 12.1795 | 1900 | 0.6214          | 0.8561   |
| 0.002         | 12.8205 | 2000 | 0.6228          | 0.8561   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1