---
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-weld-classify
  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: 0.6894977168949772
---

<!-- 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-weld-classify

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: 0.7966
- Accuracy: 0.6895

## 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: 18
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.8686        | 0.8130  | 100  | 0.7966          | 0.6895   |
| 0.6935        | 1.6260  | 200  | 1.2217          | 0.5068   |
| 0.4225        | 2.4390  | 300  | 0.9592          | 0.6210   |
| 0.2586        | 3.2520  | 400  | 1.3123          | 0.5936   |
| 0.237         | 4.0650  | 500  | 0.8075          | 0.6986   |
| 0.2658        | 4.8780  | 600  | 1.0878          | 0.6210   |
| 0.1904        | 5.6911  | 700  | 1.1048          | 0.7169   |
| 0.0964        | 6.5041  | 800  | 1.3602          | 0.6849   |
| 0.0474        | 7.3171  | 900  | 1.1331          | 0.7671   |
| 0.1179        | 8.1301  | 1000 | 1.1228          | 0.7306   |
| 0.0447        | 8.9431  | 1100 | 1.2609          | 0.7397   |
| 0.0043        | 9.7561  | 1200 | 1.1746          | 0.7763   |
| 0.1059        | 10.5691 | 1300 | 1.1867          | 0.7763   |
| 0.0026        | 11.3821 | 1400 | 1.2890          | 0.7534   |
| 0.0039        | 12.1951 | 1500 | 1.3283          | 0.7580   |
| 0.002         | 13.0081 | 1600 | 1.1871          | 0.7671   |
| 0.0019        | 13.8211 | 1700 | 1.1643          | 0.7900   |
| 0.0264        | 14.6341 | 1800 | 1.1537          | 0.7900   |
| 0.0015        | 15.4472 | 1900 | 1.1821          | 0.7945   |
| 0.0015        | 16.2602 | 2000 | 1.1962          | 0.7900   |
| 0.0014        | 17.0732 | 2100 | 1.2036          | 0.7900   |
| 0.0014        | 17.8862 | 2200 | 1.2067          | 0.7900   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1