---
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: 0.5375
---

<!-- 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: 1.2847
- Accuracy: 0.5375

## 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.856         | 1.0   | 10   | 1.9048          | 0.3063   |
| 1.81          | 2.0   | 20   | 1.8399          | 0.3063   |
| 1.7131        | 3.0   | 30   | 1.7206          | 0.3375   |
| 1.5894        | 4.0   | 40   | 1.6192          | 0.375    |
| 1.4919        | 5.0   | 50   | 1.5405          | 0.4688   |
| 1.4037        | 6.0   | 60   | 1.4735          | 0.4625   |
| 1.2923        | 7.0   | 70   | 1.4350          | 0.4688   |
| 1.2228        | 8.0   | 80   | 1.4562          | 0.4188   |
| 1.1275        | 9.0   | 90   | 1.3757          | 0.4875   |
| 1.0461        | 10.0  | 100  | 1.3880          | 0.45     |
| 0.9891        | 11.0  | 110  | 1.3440          | 0.5      |
| 0.9058        | 12.0  | 120  | 1.3576          | 0.4813   |
| 0.8835        | 13.0  | 130  | 1.3420          | 0.5188   |
| 0.8274        | 14.0  | 140  | 1.3294          | 0.4938   |
| 0.7686        | 15.0  | 150  | 1.2996          | 0.525    |
| 0.7181        | 16.0  | 160  | 1.2817          | 0.5437   |
| 0.6822        | 17.0  | 170  | 1.2726          | 0.5312   |
| 0.6398        | 18.0  | 180  | 1.3250          | 0.5062   |
| 0.6009        | 19.0  | 190  | 1.3224          | 0.5312   |
| 0.5892        | 20.0  | 200  | 1.3125          | 0.4875   |
| 0.5528        | 21.0  | 210  | 1.3334          | 0.4938   |
| 0.5699        | 22.0  | 220  | 1.2408          | 0.5563   |
| 0.5209        | 23.0  | 230  | 1.3150          | 0.525    |
| 0.5011        | 24.0  | 240  | 1.3601          | 0.4938   |
| 0.5123        | 25.0  | 250  | 1.2566          | 0.5563   |
| 0.4768        | 26.0  | 260  | 1.2542          | 0.5188   |
| 0.4812        | 27.0  | 270  | 1.2753          | 0.525    |
| 0.474         | 28.0  | 280  | 1.2961          | 0.5125   |
| 0.5015        | 29.0  | 290  | 1.2658          | 0.5437   |
| 0.4685        | 30.0  | 300  | 1.2562          | 0.55     |


### Framework versions

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