---
license: apache-2.0
base_model: google/vit-large-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Adam_ViTL-16-224-1e-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: 0.9683908045977011
---

<!-- 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. -->

# Adam_ViTL-16-224-1e-4-batch_16_epoch_4_classes_24

This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1561
- Accuracy: 0.9684

## 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: 0.0001
- 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: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7459        | 0.03  | 100  | 0.6501          | 0.8190   |
| 0.5929        | 0.07  | 200  | 0.4409          | 0.8836   |
| 0.2493        | 0.1   | 300  | 0.3525          | 0.9009   |
| 0.2142        | 0.14  | 400  | 0.3999          | 0.8779   |
| 0.3381        | 0.17  | 500  | 0.4229          | 0.8851   |
| 0.3445        | 0.21  | 600  | 0.2836          | 0.9195   |
| 0.2239        | 0.24  | 700  | 0.3989          | 0.8836   |
| 0.3475        | 0.28  | 800  | 0.2761          | 0.9210   |
| 0.0307        | 0.31  | 900  | 0.2963          | 0.9080   |
| 0.2957        | 0.35  | 1000 | 0.4865          | 0.8793   |
| 0.2431        | 0.38  | 1100 | 0.2740          | 0.9325   |
| 0.0729        | 0.42  | 1200 | 0.2630          | 0.9224   |
| 0.2757        | 0.45  | 1300 | 0.2515          | 0.9339   |
| 0.1763        | 0.49  | 1400 | 0.3826          | 0.9037   |
| 0.1481        | 0.52  | 1500 | 0.2282          | 0.9411   |
| 0.21          | 0.56  | 1600 | 0.2288          | 0.9454   |
| 0.2224        | 0.59  | 1700 | 0.3142          | 0.9296   |
| 0.0815        | 0.63  | 1800 | 0.2412          | 0.9411   |
| 0.0687        | 0.66  | 1900 | 0.2835          | 0.9353   |
| 0.3321        | 0.7   | 2000 | 0.3000          | 0.9282   |
| 0.1174        | 0.73  | 2100 | 0.2154          | 0.9440   |
| 0.0694        | 0.77  | 2200 | 0.2062          | 0.9497   |
| 0.0351        | 0.8   | 2300 | 0.1716          | 0.9511   |
| 0.088         | 0.84  | 2400 | 0.1410          | 0.9511   |
| 0.0856        | 0.87  | 2500 | 0.2342          | 0.9411   |
| 0.2248        | 0.91  | 2600 | 0.1954          | 0.9497   |
| 0.1188        | 0.94  | 2700 | 0.2655          | 0.9425   |
| 0.0322        | 0.98  | 2800 | 0.2535          | 0.9440   |
| 0.0739        | 1.01  | 2900 | 0.1640          | 0.9526   |
| 0.0352        | 1.04  | 3000 | 0.1760          | 0.9612   |
| 0.0007        | 1.08  | 3100 | 0.1593          | 0.9641   |
| 0.0107        | 1.11  | 3200 | 0.1970          | 0.9569   |
| 0.0027        | 1.15  | 3300 | 0.1603          | 0.9583   |
| 0.0005        | 1.18  | 3400 | 0.1550          | 0.9583   |
| 0.0637        | 1.22  | 3500 | 0.1874          | 0.9583   |
| 0.0006        | 1.25  | 3600 | 0.1829          | 0.9583   |
| 0.0626        | 1.29  | 3700 | 0.2311          | 0.9526   |
| 0.1023        | 1.32  | 3800 | 0.2325          | 0.9483   |
| 0.0014        | 1.36  | 3900 | 0.1556          | 0.9698   |
| 0.0186        | 1.39  | 4000 | 0.2151          | 0.9483   |
| 0.0005        | 1.43  | 4100 | 0.1369          | 0.9670   |
| 0.0005        | 1.46  | 4200 | 0.1240          | 0.9727   |
| 0.0004        | 1.5   | 4300 | 0.2019          | 0.9612   |
| 0.0008        | 1.53  | 4400 | 0.1361          | 0.9713   |
| 0.013         | 1.57  | 4500 | 0.1343          | 0.9684   |
| 0.014         | 1.6   | 4600 | 0.1553          | 0.9670   |
| 0.0005        | 1.64  | 4700 | 0.1528          | 0.9655   |
| 0.0003        | 1.67  | 4800 | 0.1586          | 0.9641   |
| 0.0009        | 1.71  | 4900 | 0.1598          | 0.9655   |
| 0.0003        | 1.74  | 5000 | 0.1727          | 0.9641   |
| 0.0003        | 1.78  | 5100 | 0.1521          | 0.9727   |
| 0.0076        | 1.81  | 5200 | 0.1534          | 0.9698   |
| 0.0003        | 1.85  | 5300 | 0.1656          | 0.9655   |
| 0.0003        | 1.88  | 5400 | 0.1833          | 0.9641   |
| 0.0003        | 1.92  | 5500 | 0.1719          | 0.9670   |
| 0.0003        | 1.95  | 5600 | 0.1565          | 0.9684   |
| 0.0003        | 1.99  | 5700 | 0.1561          | 0.9684   |


### Framework versions

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