---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: finetuned-arsenic
  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: 0.9986902423051736
---

<!-- 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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0039
- Accuracy: 0.9987

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.3535        | 0.1848 | 100  | 0.2896          | 0.8762   |
| 0.1869        | 0.3697 | 200  | 0.1380          | 0.9574   |
| 0.1594        | 0.5545 | 300  | 0.1052          | 0.9679   |
| 0.117         | 0.7394 | 400  | 0.0502          | 0.9836   |
| 0.0796        | 0.9242 | 500  | 0.0881          | 0.9673   |
| 0.0795        | 1.1091 | 600  | 0.0698          | 0.9751   |
| 0.0644        | 1.2939 | 700  | 0.0342          | 0.9895   |
| 0.054         | 1.4787 | 800  | 0.0344          | 0.9882   |
| 0.0776        | 1.6636 | 900  | 0.0292          | 0.9915   |
| 0.0143        | 1.8484 | 1000 | 0.0242          | 0.9928   |
| 0.0597        | 2.0333 | 1100 | 0.0132          | 0.9954   |
| 0.0285        | 2.2181 | 1200 | 0.0263          | 0.9928   |
| 0.0349        | 2.4030 | 1300 | 0.0070          | 0.9980   |
| 0.0164        | 2.5878 | 1400 | 0.0067          | 0.9987   |
| 0.0058        | 2.7726 | 1500 | 0.0119          | 0.9954   |
| 0.0013        | 2.9575 | 1600 | 0.0066          | 0.9987   |
| 0.0028        | 3.1423 | 1700 | 0.0052          | 0.9987   |
| 0.0028        | 3.3272 | 1800 | 0.0052          | 0.9987   |
| 0.0244        | 3.5120 | 1900 | 0.0264          | 0.9928   |
| 0.002         | 3.6969 | 2000 | 0.0025          | 0.9993   |
| 0.0054        | 3.8817 | 2100 | 0.0039          | 0.9987   |


### Framework versions

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