---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: image_classification
  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: 0.59375
---

<!-- 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. -->

# image_classification

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: 1.2364
- Accuracy: 0.5938

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0702        | 1.0   | 10   | 2.0666          | 0.1437   |
| 2.0583        | 2.0   | 20   | 2.0476          | 0.2125   |
| 2.0291        | 3.0   | 30   | 2.0018          | 0.3      |
| 1.9639        | 4.0   | 40   | 1.9175          | 0.3563   |
| 1.8582        | 5.0   | 50   | 1.7997          | 0.4375   |
| 1.7385        | 6.0   | 60   | 1.6756          | 0.4625   |
| 1.5984        | 7.0   | 70   | 1.5469          | 0.4625   |
| 1.4739        | 8.0   | 80   | 1.4684          | 0.5188   |
| 1.3737        | 9.0   | 90   | 1.4090          | 0.5125   |
| 1.2719        | 10.0  | 100  | 1.3740          | 0.525    |
| 1.2072        | 11.0  | 110  | 1.3527          | 0.55     |
| 1.1158        | 12.0  | 120  | 1.3118          | 0.5188   |
| 1.0487        | 13.0  | 130  | 1.2349          | 0.6      |
| 0.9873        | 14.0  | 140  | 1.2931          | 0.525    |
| 0.8928        | 15.0  | 150  | 1.2731          | 0.55     |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1