transformers tensorboard safetensors vit image-classification vision 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:
- image-classification
- vision
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-letter-identification-v2
  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: 0.8627450980392157
---

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# vit-letter-identification-v2

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: 1.1135
- Accuracy: 0.8627

## 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: 2e-05
- train_batch_size: 100
- eval_batch_size: 102
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 120.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 6    | 3.2331          | 0.0882   |
| 3.2363        | 2.0   | 12   | 3.2025          | 0.1373   |
| 3.2363        | 3.0   | 18   | 3.1761          | 0.1863   |
| 3.1622        | 4.0   | 24   | 3.1238          | 0.2255   |
| 3.0918        | 5.0   | 30   | 3.0789          | 0.3137   |
| 3.0918        | 6.0   | 36   | 3.0280          | 0.3235   |
| 3.0081        | 7.0   | 42   | 2.9878          | 0.3431   |
| 3.0081        | 8.0   | 48   | 2.9316          | 0.3824   |
| 2.9118        | 9.0   | 54   | 2.8864          | 0.4314   |
| 2.8231        | 10.0  | 60   | 2.8314          | 0.4510   |
| 2.8231        | 11.0  | 66   | 2.7817          | 0.5196   |
| 2.7149        | 12.0  | 72   | 2.7278          | 0.5196   |
| 2.7149        | 13.0  | 78   | 2.6796          | 0.5588   |
| 2.6202        | 14.0  | 84   | 2.6203          | 0.5882   |
| 2.5243        | 15.0  | 90   | 2.5674          | 0.5882   |
| 2.5243        | 16.0  | 96   | 2.5170          | 0.6078   |
| 2.4279        | 17.0  | 102  | 2.4672          | 0.6176   |
| 2.4279        | 18.0  | 108  | 2.4285          | 0.5980   |
| 2.3404        | 19.0  | 114  | 2.3784          | 0.6569   |
| 2.2633        | 20.0  | 120  | 2.3348          | 0.6471   |
| 2.2633        | 21.0  | 126  | 2.2872          | 0.6667   |
| 2.1838        | 22.0  | 132  | 2.2539          | 0.6569   |
| 2.1838        | 23.0  | 138  | 2.2232          | 0.6765   |
| 2.1022        | 24.0  | 144  | 2.1867          | 0.6471   |
| 2.0364        | 25.0  | 150  | 2.1489          | 0.6863   |
| 2.0364        | 26.0  | 156  | 2.1099          | 0.7255   |
| 1.96          | 27.0  | 162  | 2.0767          | 0.7157   |
| 1.96          | 28.0  | 168  | 2.0417          | 0.7157   |
| 1.9235        | 29.0  | 174  | 2.0162          | 0.7353   |
| 1.8484        | 30.0  | 180  | 1.9787          | 0.7451   |
| 1.8484        | 31.0  | 186  | 1.9548          | 0.7451   |
| 1.7971        | 32.0  | 192  | 1.9329          | 0.7549   |
| 1.7971        | 33.0  | 198  | 1.9052          | 0.7647   |
| 1.7409        | 34.0  | 204  | 1.8827          | 0.7549   |
| 1.7006        | 35.0  | 210  | 1.8589          | 0.7745   |
| 1.7006        | 36.0  | 216  | 1.8294          | 0.7843   |
| 1.6426        | 37.0  | 222  | 1.8098          | 0.7843   |
| 1.6426        | 38.0  | 228  | 1.7809          | 0.7647   |
| 1.6102        | 39.0  | 234  | 1.7643          | 0.7843   |
| 1.5704        | 40.0  | 240  | 1.7399          | 0.8039   |
| 1.5704        | 41.0  | 246  | 1.7193          | 0.8137   |
| 1.5264        | 42.0  | 252  | 1.6980          | 0.8333   |
| 1.5264        | 43.0  | 258  | 1.6840          | 0.8039   |
| 1.4821        | 44.0  | 264  | 1.6644          | 0.8235   |
| 1.4506        | 45.0  | 270  | 1.6467          | 0.8235   |
| 1.4506        | 46.0  | 276  | 1.6333          | 0.8235   |
| 1.4358        | 47.0  | 282  | 1.6095          | 0.8235   |
| 1.4358        | 48.0  | 288  | 1.5906          | 0.8235   |
| 1.3695        | 49.0  | 294  | 1.5720          | 0.8431   |
| 1.367         | 50.0  | 300  | 1.5610          | 0.8333   |
| 1.367         | 51.0  | 306  | 1.5440          | 0.8529   |
| 1.3299        | 52.0  | 312  | 1.5359          | 0.8333   |
| 1.3299        | 53.0  | 318  | 1.5129          | 0.8333   |
| 1.2765        | 54.0  | 324  | 1.5057          | 0.8235   |
| 1.2785        | 55.0  | 330  | 1.4867          | 0.8235   |
| 1.2785        | 56.0  | 336  | 1.4751          | 0.8333   |
| 1.2355        | 57.0  | 342  | 1.4553          | 0.8235   |
| 1.2355        | 58.0  | 348  | 1.4491          | 0.8235   |
| 1.2418        | 59.0  | 354  | 1.4289          | 0.8431   |
| 1.2058        | 60.0  | 360  | 1.4185          | 0.8235   |
| 1.2058        | 61.0  | 366  | 1.4104          | 0.8333   |
| 1.164         | 62.0  | 372  | 1.3968          | 0.8333   |
| 1.164         | 63.0  | 378  | 1.3846          | 0.8431   |
| 1.1529        | 64.0  | 384  | 1.3697          | 0.8431   |
| 1.1408        | 65.0  | 390  | 1.3633          | 0.8431   |
| 1.1408        | 66.0  | 396  | 1.3505          | 0.8431   |
| 1.1102        | 67.0  | 402  | 1.3371          | 0.8529   |
| 1.1102        | 68.0  | 408  | 1.3282          | 0.8529   |
| 1.0906        | 69.0  | 414  | 1.3240          | 0.8431   |
| 1.0759        | 70.0  | 420  | 1.3163          | 0.8431   |
| 1.0759        | 71.0  | 426  | 1.3044          | 0.8529   |
| 1.0651        | 72.0  | 432  | 1.2924          | 0.8431   |
| 1.0651        | 73.0  | 438  | 1.2867          | 0.8529   |
| 1.0501        | 74.0  | 444  | 1.2749          | 0.8529   |
| 1.0238        | 75.0  | 450  | 1.2688          | 0.8431   |
| 1.0238        | 76.0  | 456  | 1.2568          | 0.8529   |
| 1.0046        | 77.0  | 462  | 1.2502          | 0.8529   |
| 1.0046        | 78.0  | 468  | 1.2460          | 0.8529   |
| 0.9946        | 79.0  | 474  | 1.2455          | 0.8431   |
| 0.9998        | 80.0  | 480  | 1.2343          | 0.8529   |
| 0.9998        | 81.0  | 486  | 1.2286          | 0.8529   |
| 0.9709        | 82.0  | 492  | 1.2195          | 0.8431   |
| 0.9709        | 83.0  | 498  | 1.2126          | 0.8529   |
| 0.963         | 84.0  | 504  | 1.2102          | 0.8431   |
| 0.9499        | 85.0  | 510  | 1.2024          | 0.8431   |
| 0.9499        | 86.0  | 516  | 1.1980          | 0.8529   |
| 0.937         | 87.0  | 522  | 1.1912          | 0.8529   |
| 0.937         | 88.0  | 528  | 1.1883          | 0.8431   |
| 0.9389        | 89.0  | 534  | 1.1845          | 0.8529   |
| 0.9181        | 90.0  | 540  | 1.1811          | 0.8529   |
| 0.9181        | 91.0  | 546  | 1.1777          | 0.8431   |
| 0.9219        | 92.0  | 552  | 1.1743          | 0.8627   |
| 0.9219        | 93.0  | 558  | 1.1675          | 0.8627   |
| 0.9067        | 94.0  | 564  | 1.1598          | 0.8627   |
| 0.9009        | 95.0  | 570  | 1.1601          | 0.8627   |
| 0.9009        | 96.0  | 576  | 1.1564          | 0.8529   |
| 0.8914        | 97.0  | 582  | 1.1505          | 0.8529   |
| 0.8914        | 98.0  | 588  | 1.1487          | 0.8529   |
| 0.8739        | 99.0  | 594  | 1.1480          | 0.8627   |
| 0.8742        | 100.0 | 600  | 1.1413          | 0.8529   |
| 0.8742        | 101.0 | 606  | 1.1368          | 0.8627   |
| 0.8679        | 102.0 | 612  | 1.1361          | 0.8627   |
| 0.8679        | 103.0 | 618  | 1.1317          | 0.8627   |
| 0.8516        | 104.0 | 624  | 1.1296          | 0.8529   |
| 0.876         | 105.0 | 630  | 1.1288          | 0.8627   |
| 0.876         | 106.0 | 636  | 1.1264          | 0.8627   |
| 0.8591        | 107.0 | 642  | 1.1238          | 0.8627   |
| 0.8591        | 108.0 | 648  | 1.1227          | 0.8627   |
| 0.8586        | 109.0 | 654  | 1.1208          | 0.8627   |
| 0.8415        | 110.0 | 660  | 1.1194          | 0.8627   |
| 0.8415        | 111.0 | 666  | 1.1185          | 0.8627   |
| 0.8465        | 112.0 | 672  | 1.1178          | 0.8529   |
| 0.8465        | 113.0 | 678  | 1.1184          | 0.8529   |
| 0.8503        | 114.0 | 684  | 1.1183          | 0.8431   |
| 0.8332        | 115.0 | 690  | 1.1174          | 0.8431   |
| 0.8332        | 116.0 | 696  | 1.1165          | 0.8431   |
| 0.8476        | 117.0 | 702  | 1.1153          | 0.8529   |
| 0.8476        | 118.0 | 708  | 1.1142          | 0.8529   |
| 0.8382        | 119.0 | 714  | 1.1137          | 0.8627   |
| 0.8527        | 120.0 | 720  | 1.1135          | 0.8627   |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
