---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-dmae-va-da2-40
  results: []
---

<!-- 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-dmae-va-da2-40

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co./google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2660
- Accuracy: 0.9655

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.94  | 4    | 1.2931          | 0.4310   |
| No log        | 1.88  | 8    | 1.2024          | 0.5517   |
| 1.2651        | 2.82  | 12   | 0.9896          | 0.6552   |
| 1.2651        | 4.0   | 17   | 0.7972          | 0.7241   |
| 1.2651        | 4.94  | 21   | 0.7336          | 0.6552   |
| 0.7523        | 5.88  | 25   | 0.5781          | 0.8103   |
| 0.7523        | 6.82  | 29   | 0.4912          | 0.8793   |
| 0.7523        | 8.0   | 34   | 0.4112          | 0.9138   |
| 0.4209        | 8.94  | 38   | 0.3383          | 0.9138   |
| 0.4209        | 9.88  | 42   | 0.3129          | 0.9483   |
| 0.4209        | 10.82 | 46   | 0.2660          | 0.9655   |
| 0.2647        | 12.0  | 51   | 0.3184          | 0.9310   |
| 0.2647        | 12.94 | 55   | 0.2871          | 0.9310   |
| 0.2647        | 13.88 | 59   | 0.2766          | 0.9138   |
| 0.1743        | 14.82 | 63   | 0.2727          | 0.8966   |
| 0.1743        | 16.0  | 68   | 0.2282          | 0.9310   |
| 0.1511        | 16.94 | 72   | 0.2892          | 0.8966   |
| 0.1511        | 17.88 | 76   | 0.2482          | 0.8966   |
| 0.1511        | 18.82 | 80   | 0.2363          | 0.9310   |
| 0.1253        | 20.0  | 85   | 0.1622          | 0.9483   |
| 0.1253        | 20.94 | 89   | 0.1753          | 0.9483   |
| 0.1253        | 21.88 | 93   | 0.1593          | 0.9655   |
| 0.087         | 22.82 | 97   | 0.1334          | 0.9483   |
| 0.087         | 24.0  | 102  | 0.1088          | 0.9483   |
| 0.087         | 24.94 | 106  | 0.1130          | 0.9483   |
| 0.0856        | 25.88 | 110  | 0.1459          | 0.9138   |
| 0.0856        | 26.82 | 114  | 0.1445          | 0.9655   |
| 0.0856        | 28.0  | 119  | 0.1234          | 0.9655   |
| 0.081         | 28.94 | 123  | 0.1224          | 0.9483   |
| 0.081         | 29.88 | 127  | 0.1303          | 0.9483   |
| 0.081         | 30.82 | 131  | 0.1372          | 0.9483   |
| 0.0554        | 32.0  | 136  | 0.1421          | 0.9483   |
| 0.0554        | 32.94 | 140  | 0.1307          | 0.9483   |
| 0.0783        | 33.88 | 144  | 0.1244          | 0.9483   |
| 0.0783        | 34.82 | 148  | 0.1195          | 0.9483   |
| 0.0783        | 36.0  | 153  | 0.1171          | 0.9483   |
| 0.0646        | 36.94 | 157  | 0.1165          | 0.9483   |
| 0.0646        | 37.65 | 160  | 0.1163          | 0.9483   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1