---
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: 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: 0.63125
---

<!-- 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: 1.1383
- Accuracy: 0.6312

## 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: 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.925         | 1.0   | 10   | 1.3570          | 0.4688   |
| 0.8379        | 2.0   | 20   | 1.1685          | 0.5875   |
| 0.6737        | 3.0   | 30   | 1.1795          | 0.6      |
| 0.4606        | 4.0   | 40   | 1.1383          | 0.6312   |
| 0.3416        | 5.0   | 50   | 1.2393          | 0.5687   |
| 0.2493        | 6.0   | 60   | 1.3971          | 0.5938   |
| 0.2341        | 7.0   | 70   | 1.3546          | 0.6062   |
| 0.1797        | 8.0   | 80   | 1.3681          | 0.5938   |
| 0.1221        | 9.0   | 90   | 1.6936          | 0.525    |
| 0.1077        | 10.0  | 100  | 1.7008          | 0.5375   |
| 0.0966        | 11.0  | 110  | 1.7380          | 0.525    |
| 0.1073        | 12.0  | 120  | 1.5617          | 0.575    |
| 0.0849        | 13.0  | 130  | 1.6178          | 0.6125   |
| 0.0704        | 14.0  | 140  | 1.6144          | 0.6125   |
| 0.0568        | 15.0  | 150  | 1.6111          | 0.6188   |
| 0.0555        | 16.0  | 160  | 1.5946          | 0.6      |
| 0.0498        | 17.0  | 170  | 1.6291          | 0.625    |
| 0.0464        | 18.0  | 180  | 1.6574          | 0.6188   |
| 0.0443        | 19.0  | 190  | 1.6740          | 0.6125   |
| 0.0429        | 20.0  | 200  | 1.6781          | 0.6125   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1