---
license: apache-2.0
base_model: google/vit-large-patch16-384
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vitLarge-16-384-2e-4-batch_16_epoch_4_classes_24
  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: 0.9712643678160919
---

<!-- 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. -->

# vitLarge-16-384-2e-4-batch_16_epoch_4_classes_24

This model is a fine-tuned version of [google/vit-large-patch16-384](https://huggingface.co/google/vit-large-patch16-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1519
- Accuracy: 0.9713

## 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: 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: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7944        | 0.03  | 100  | 0.8483          | 0.7428   |
| 1.1261        | 0.07  | 200  | 1.0595          | 0.6911   |
| 0.7575        | 0.1   | 300  | 0.5007          | 0.8534   |
| 0.3567        | 0.14  | 400  | 0.5404          | 0.8391   |
| 0.4062        | 0.17  | 500  | 0.7795          | 0.7974   |
| 0.4227        | 0.21  | 600  | 0.3598          | 0.8851   |
| 0.3436        | 0.24  | 700  | 0.4550          | 0.8693   |
| 0.7695        | 0.28  | 800  | 0.5748          | 0.8247   |
| 0.2864        | 0.31  | 900  | 0.4017          | 0.8793   |
| 0.3718        | 0.35  | 1000 | 0.5384          | 0.8420   |
| 0.2764        | 0.38  | 1100 | 0.4682          | 0.8764   |
| 0.3438        | 0.42  | 1200 | 0.4194          | 0.8807   |
| 0.4031        | 0.45  | 1300 | 0.4105          | 0.8922   |
| 0.449         | 0.49  | 1400 | 0.4499          | 0.8678   |
| 0.2249        | 0.52  | 1500 | 0.2701          | 0.9066   |
| 0.2398        | 0.56  | 1600 | 0.4124          | 0.8807   |
| 0.5759        | 0.59  | 1700 | 0.8378          | 0.7960   |
| 0.1315        | 0.63  | 1800 | 0.4757          | 0.8779   |
| 0.4481        | 0.66  | 1900 | 0.3463          | 0.9037   |
| 0.2183        | 0.7   | 2000 | 0.4291          | 0.8779   |
| 0.2101        | 0.73  | 2100 | 0.3318          | 0.9109   |
| 1.0071        | 0.77  | 2200 | 2.9399          | 0.2098   |
| 0.3426        | 0.8   | 2300 | 0.4231          | 0.9023   |
| 0.1126        | 0.84  | 2400 | 0.3609          | 0.9124   |
| 0.3954        | 0.87  | 2500 | 0.4471          | 0.8994   |
| 0.2099        | 0.91  | 2600 | 0.3465          | 0.9052   |
| 0.1982        | 0.94  | 2700 | 0.4135          | 0.8994   |
| 0.1931        | 0.98  | 2800 | 0.3306          | 0.9095   |
| 0.1721        | 1.01  | 2900 | 0.3470          | 0.9195   |
| 0.1864        | 1.04  | 3000 | 0.3814          | 0.9124   |
| 0.0652        | 1.08  | 3100 | 0.2534          | 0.9296   |
| 0.1176        | 1.11  | 3200 | 0.2744          | 0.9210   |
| 0.0988        | 1.15  | 3300 | 0.2966          | 0.9325   |
| 0.0289        | 1.18  | 3400 | 0.2021          | 0.9555   |
| 0.1465        | 1.22  | 3500 | 0.1566          | 0.9583   |
| 0.2023        | 1.25  | 3600 | 0.2803          | 0.9353   |
| 0.1042        | 1.29  | 3700 | 0.2893          | 0.9282   |
| 0.1403        | 1.32  | 3800 | 0.3145          | 0.9239   |
| 0.0786        | 1.36  | 3900 | 0.3188          | 0.9267   |
| 0.2427        | 1.39  | 4000 | 0.6615          | 0.8693   |
| 0.3187        | 1.43  | 4100 | 0.3598          | 0.9195   |
| 0.0897        | 1.46  | 4200 | 0.2778          | 0.9425   |
| 0.068         | 1.5   | 4300 | 0.3445          | 0.9124   |
| 0.2165        | 1.53  | 4400 | 0.2351          | 0.9468   |
| 0.0807        | 1.57  | 4500 | 0.3111          | 0.9310   |
| 0.007         | 1.6   | 4600 | 0.2208          | 0.9483   |
| 0.0017        | 1.64  | 4700 | 0.1943          | 0.9411   |
| 0.081         | 1.67  | 4800 | 0.3503          | 0.9239   |
| 0.0285        | 1.71  | 4900 | 0.3109          | 0.9239   |
| 0.0495        | 1.74  | 5000 | 0.1233          | 0.9641   |
| 0.0201        | 1.78  | 5100 | 0.2508          | 0.9483   |
| 0.1186        | 1.81  | 5200 | 0.3854          | 0.9210   |
| 0.0283        | 1.85  | 5300 | 0.2336          | 0.9425   |
| 0.0569        | 1.88  | 5400 | 0.2872          | 0.9425   |
| 0.0498        | 1.92  | 5500 | 0.2462          | 0.9569   |
| 0.0101        | 1.95  | 5600 | 0.2256          | 0.9511   |
| 0.0474        | 1.99  | 5700 | 0.2201          | 0.9569   |
| 0.0008        | 2.02  | 5800 | 0.2079          | 0.9526   |
| 0.0           | 2.06  | 5900 | 0.1951          | 0.9583   |
| 0.0007        | 2.09  | 6000 | 0.1449          | 0.9626   |
| 0.003         | 2.12  | 6100 | 0.1411          | 0.9670   |
| 0.0028        | 2.16  | 6200 | 0.1889          | 0.9598   |
| 0.0018        | 2.19  | 6300 | 0.2356          | 0.9511   |
| 0.0087        | 2.23  | 6400 | 0.2185          | 0.9569   |
| 0.0169        | 2.26  | 6500 | 0.1898          | 0.9583   |
| 0.0003        | 2.3   | 6600 | 0.1879          | 0.9655   |
| 0.0008        | 2.33  | 6700 | 0.1331          | 0.9713   |
| 0.0001        | 2.37  | 6800 | 0.1537          | 0.9655   |
| 0.0002        | 2.4   | 6900 | 0.2148          | 0.9598   |
| 0.0079        | 2.44  | 7000 | 0.1258          | 0.9698   |
| 0.0004        | 2.47  | 7100 | 0.1557          | 0.9698   |
| 0.0           | 2.51  | 7200 | 0.1376          | 0.9698   |
| 0.0007        | 2.54  | 7300 | 0.1238          | 0.9713   |
| 0.0           | 2.58  | 7400 | 0.1433          | 0.9670   |
| 0.0023        | 2.61  | 7500 | 0.1537          | 0.9684   |
| 0.0004        | 2.65  | 7600 | 0.1302          | 0.9727   |
| 0.0002        | 2.68  | 7700 | 0.1557          | 0.9698   |
| 0.0013        | 2.72  | 7800 | 0.1614          | 0.9698   |
| 0.0           | 2.75  | 7900 | 0.1713          | 0.9670   |
| 0.0           | 2.79  | 8000 | 0.1458          | 0.9698   |
| 0.0           | 2.82  | 8100 | 0.1453          | 0.9698   |
| 0.0           | 2.86  | 8200 | 0.1527          | 0.9670   |
| 0.0           | 2.89  | 8300 | 0.1508          | 0.9698   |
| 0.0001        | 2.93  | 8400 | 0.1544          | 0.9713   |
| 0.0           | 2.96  | 8500 | 0.1506          | 0.9713   |
| 0.0           | 3.0   | 8600 | 0.1519          | 0.9713   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2