---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
model-index:
- name: vit-base-patch16-224
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.79
    - name: Precision
      type: precision
      value: 0.7955164222268126
    - name: Recall
      type: recall
      value: 0.79
---

<!-- 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. -->

# vit-base-patch16-224

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co./google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6740
- Accuracy: 0.79
- Precision: 0.7955
- Recall: 0.79
- F1 Score: 0.7923

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log        | 1.0   | 4    | 0.5895          | 0.725    | 0.5256    | 0.725  | 0.6094   |
| No log        | 2.0   | 8    | 0.5737          | 0.725    | 0.5256    | 0.725  | 0.6094   |
| No log        | 3.0   | 12   | 0.5746          | 0.7333   | 0.6978    | 0.7333 | 0.6589   |
| No log        | 4.0   | 16   | 0.5449          | 0.7292   | 0.7126    | 0.7292 | 0.6263   |
| No log        | 5.0   | 20   | 0.5943          | 0.7208   | 0.7362    | 0.7208 | 0.7270   |
| No log        | 6.0   | 24   | 0.5124          | 0.75     | 0.7360    | 0.75   | 0.6895   |
| No log        | 7.0   | 28   | 0.6057          | 0.6625   | 0.7301    | 0.6625 | 0.6797   |
| No log        | 8.0   | 32   | 0.5059          | 0.7583   | 0.7376    | 0.7583 | 0.7214   |
| No log        | 9.0   | 36   | 0.5734          | 0.7125   | 0.7474    | 0.7125 | 0.7237   |
| No log        | 10.0  | 40   | 0.5069          | 0.7458   | 0.7182    | 0.7458 | 0.7116   |
| No log        | 11.0  | 44   | 0.5135          | 0.775    | 0.7659    | 0.775  | 0.7689   |
| No log        | 12.0  | 48   | 0.4943          | 0.775    | 0.7601    | 0.775  | 0.7610   |
| 0.5275        | 13.0  | 52   | 0.5654          | 0.7458   | 0.7790    | 0.7458 | 0.7557   |
| 0.5275        | 14.0  | 56   | 0.5257          | 0.7625   | 0.7636    | 0.7625 | 0.7631   |
| 0.5275        | 15.0  | 60   | 0.5107          | 0.7875   | 0.7813    | 0.7875 | 0.7836   |
| 0.5275        | 16.0  | 64   | 0.5514          | 0.7333   | 0.7655    | 0.7333 | 0.7434   |
| 0.5275        | 17.0  | 68   | 0.5004          | 0.7833   | 0.7698    | 0.7833 | 0.7699   |
| 0.5275        | 18.0  | 72   | 0.5999          | 0.7125   | 0.7738    | 0.7125 | 0.7269   |
| 0.5275        | 19.0  | 76   | 0.4975          | 0.7667   | 0.7554    | 0.7667 | 0.7589   |
| 0.5275        | 20.0  | 80   | 0.5120          | 0.7917   | 0.7981    | 0.7917 | 0.7944   |
| 0.5275        | 21.0  | 84   | 0.5203          | 0.7833   | 0.7876    | 0.7833 | 0.7853   |
| 0.5275        | 22.0  | 88   | 0.5304          | 0.8042   | 0.8051    | 0.8042 | 0.8046   |
| 0.5275        | 23.0  | 92   | 0.5475          | 0.825    | 0.825     | 0.825  | 0.8250   |
| 0.5275        | 24.0  | 96   | 0.5757          | 0.7458   | 0.7661    | 0.7458 | 0.7531   |
| 0.2422        | 25.0  | 100  | 0.5669          | 0.7875   | 0.7829    | 0.7875 | 0.7848   |
| 0.2422        | 26.0  | 104  | 0.5489          | 0.7958   | 0.7931    | 0.7958 | 0.7943   |
| 0.2422        | 27.0  | 108  | 0.5372          | 0.8      | 0.7982    | 0.8    | 0.7990   |
| 0.2422        | 28.0  | 112  | 0.5500          | 0.8208   | 0.8160    | 0.8208 | 0.8176   |
| 0.2422        | 29.0  | 116  | 0.5682          | 0.8042   | 0.8033    | 0.8042 | 0.8037   |
| 0.2422        | 30.0  | 120  | 0.5899          | 0.8083   | 0.8050    | 0.8083 | 0.8064   |
| 0.2422        | 31.0  | 124  | 0.6217          | 0.8      | 0.8063    | 0.8    | 0.8026   |
| 0.2422        | 32.0  | 128  | 0.6063          | 0.8125   | 0.8053    | 0.8125 | 0.8068   |
| 0.2422        | 33.0  | 132  | 0.5843          | 0.8042   | 0.8033    | 0.8042 | 0.8037   |
| 0.2422        | 34.0  | 136  | 0.6020          | 0.8125   | 0.8073    | 0.8125 | 0.8091   |
| 0.2422        | 35.0  | 140  | 0.6180          | 0.8042   | 0.8092    | 0.8042 | 0.8063   |
| 0.2422        | 36.0  | 144  | 0.6287          | 0.8208   | 0.8171    | 0.8208 | 0.8186   |
| 0.2422        | 37.0  | 148  | 0.6231          | 0.825    | 0.8234    | 0.825  | 0.8242   |
| 0.0631        | 38.0  | 152  | 0.6260          | 0.8292   | 0.8300    | 0.8292 | 0.8296   |
| 0.0631        | 39.0  | 156  | 0.6278          | 0.8333   | 0.8294    | 0.8333 | 0.8308   |
| 0.0631        | 40.0  | 160  | 0.6325          | 0.8208   | 0.8200    | 0.8208 | 0.8204   |
| 0.0631        | 41.0  | 164  | 0.6370          | 0.8083   | 0.8013    | 0.8083 | 0.8032   |
| 0.0631        | 42.0  | 168  | 0.6371          | 0.8125   | 0.8100    | 0.8125 | 0.8111   |
| 0.0631        | 43.0  | 172  | 0.6404          | 0.8042   | 0.8016    | 0.8042 | 0.8027   |
| 0.0631        | 44.0  | 176  | 0.6640          | 0.8292   | 0.8227    | 0.8292 | 0.8229   |
| 0.0631        | 45.0  | 180  | 0.6636          | 0.8208   | 0.8185    | 0.8208 | 0.8195   |
| 0.0631        | 46.0  | 184  | 0.6826          | 0.8083   | 0.8122    | 0.8083 | 0.8100   |
| 0.0631        | 47.0  | 188  | 0.6756          | 0.8208   | 0.8185    | 0.8208 | 0.8195   |
| 0.0631        | 48.0  | 192  | 0.6695          | 0.8292   | 0.8246    | 0.8292 | 0.8261   |
| 0.0631        | 49.0  | 196  | 0.6669          | 0.825    | 0.8198    | 0.825  | 0.8213   |
| 0.0264        | 50.0  | 200  | 0.6658          | 0.825    | 0.8198    | 0.825  | 0.8213   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3