---
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: finetuned-arsenic
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: arsenic_images
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9993451211525868
---

<!-- 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. -->

# finetuned-arsenic

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 arsenic_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0026
- Accuracy: 0.9993

## 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2214        | 0.1848 | 100  | 0.1243          | 0.9607   |
| 0.1213        | 0.3697 | 200  | 0.1763          | 0.9339   |
| 0.1201        | 0.5545 | 300  | 0.1018          | 0.9607   |
| 0.0991        | 0.7394 | 400  | 0.2071          | 0.9417   |
| 0.1127        | 0.9242 | 500  | 0.0886          | 0.9666   |
| 0.0314        | 1.1091 | 600  | 0.0333          | 0.9908   |
| 0.0252        | 1.2939 | 700  | 0.0110          | 0.9974   |
| 0.0582        | 1.4787 | 800  | 0.0104          | 0.9987   |
| 0.0455        | 1.6636 | 900  | 0.0198          | 0.9954   |
| 0.0569        | 1.8484 | 1000 | 0.0180          | 0.9961   |
| 0.0627        | 2.0333 | 1100 | 0.0244          | 0.9948   |
| 0.0328        | 2.2181 | 1200 | 0.0054          | 0.9987   |
| 0.0156        | 2.4030 | 1300 | 0.0193          | 0.9948   |
| 0.0016        | 2.5878 | 1400 | 0.0074          | 0.9974   |
| 0.0032        | 2.7726 | 1500 | 0.0045          | 0.9980   |
| 0.0233        | 2.9575 | 1600 | 0.0029          | 0.9993   |
| 0.0434        | 3.1423 | 1700 | 0.0026          | 0.9993   |
| 0.0079        | 3.3272 | 1800 | 0.0095          | 0.9980   |
| 0.0175        | 3.5120 | 1900 | 0.0111          | 0.9974   |
| 0.0013        | 3.6969 | 2000 | 0.0109          | 0.9974   |
| 0.0008        | 3.8817 | 2100 | 0.0053          | 0.9987   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1