---
library_name: transformers
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-weldclassifyv4
  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: 0.920863309352518
---

<!-- 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-weldclassifyv4

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: 0.3301
- Accuracy: 0.9209

## 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.8207        | 0.6410 | 100  | 1.0336          | 0.5647   |
| 0.6506        | 1.2821 | 200  | 1.1982          | 0.5791   |
| 0.5324        | 1.9231 | 300  | 0.6060          | 0.7770   |
| 0.2486        | 2.5641 | 400  | 0.7294          | 0.7518   |
| 0.1366        | 3.2051 | 500  | 0.4832          | 0.8417   |
| 0.3124        | 3.8462 | 600  | 0.8676          | 0.7626   |
| 0.0296        | 4.4872 | 700  | 0.4233          | 0.8885   |
| 0.0723        | 5.1282 | 800  | 0.4470          | 0.8849   |
| 0.0342        | 5.7692 | 900  | 0.3406          | 0.9173   |
| 0.0055        | 6.4103 | 1000 | 0.3301          | 0.9209   |
| 0.0048        | 7.0513 | 1100 | 0.3471          | 0.9173   |
| 0.0036        | 7.6923 | 1200 | 0.3346          | 0.9137   |
| 0.003         | 8.3333 | 1300 | 0.3498          | 0.9137   |
| 0.003         | 8.9744 | 1400 | 0.3549          | 0.9101   |
| 0.0027        | 9.6154 | 1500 | 0.3569          | 0.9137   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1