transformers tensorboard safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:google/vit-base-patch16-224-in21k license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: model
  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: 0.6
---

<!-- 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. -->

# model

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: 1.4897
- Accuracy: 0.6

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 80   | 1.7001          | 0.325    |
| No log        | 2.0   | 160  | 1.4642          | 0.4875   |
| No log        | 3.0   | 240  | 1.3522          | 0.4625   |
| No log        | 4.0   | 320  | 1.3493          | 0.4688   |
| No log        | 5.0   | 400  | 1.2052          | 0.55     |
| No log        | 6.0   | 480  | 1.2267          | 0.5563   |
| 1.2917        | 7.0   | 560  | 1.1744          | 0.6062   |
| 1.2917        | 8.0   | 640  | 1.2969          | 0.5437   |
| 1.2917        | 9.0   | 720  | 1.2519          | 0.5687   |
| 1.2917        | 10.0  | 800  | 1.3108          | 0.5125   |
| 1.2917        | 11.0  | 880  | 1.2725          | 0.5875   |
| 1.2917        | 12.0  | 960  | 1.3437          | 0.55     |
| 0.5002        | 13.0  | 1040 | 1.3790          | 0.5375   |
| 0.5002        | 14.0  | 1120 | 1.3432          | 0.625    |
| 0.5002        | 15.0  | 1200 | 1.4395          | 0.55     |
| 0.5002        | 16.0  | 1280 | 1.3672          | 0.5875   |
| 0.5002        | 17.0  | 1360 | 1.3928          | 0.575    |
| 0.5002        | 18.0  | 1440 | 1.3016          | 0.5875   |
| 0.2523        | 19.0  | 1520 | 1.4815          | 0.5625   |
| 0.2523        | 20.0  | 1600 | 1.3394          | 0.6062   |
| 0.2523        | 21.0  | 1680 | 1.3450          | 0.5938   |
| 0.2523        | 22.0  | 1760 | 1.3924          | 0.6312   |
| 0.2523        | 23.0  | 1840 | 1.4664          | 0.5813   |
| 0.2523        | 24.0  | 1920 | 1.2635          | 0.65     |
| 0.1723        | 25.0  | 2000 | 1.4154          | 0.5625   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
