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---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-ve-Ub
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: 0.7254901960784313
---
<!-- 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-ve-Ub
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: 0.8470
- Accuracy: 0.7255
## 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: 80
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.57 | 1 | 1.3863 | 0.0980 |
| No log | 1.71 | 3 | 1.3813 | 0.4706 |
| No log | 2.86 | 5 | 1.3686 | 0.4706 |
| No log | 4.0 | 7 | 1.3480 | 0.4706 |
| No log | 4.57 | 8 | 1.3345 | 0.4706 |
| 1.3658 | 5.71 | 10 | 1.3040 | 0.4706 |
| 1.3658 | 6.86 | 12 | 1.2754 | 0.4706 |
| 1.3658 | 8.0 | 14 | 1.2477 | 0.4902 |
| 1.3658 | 8.57 | 15 | 1.2347 | 0.5294 |
| 1.3658 | 9.71 | 17 | 1.2109 | 0.5490 |
| 1.3658 | 10.86 | 19 | 1.1889 | 0.6078 |
| 1.2512 | 12.0 | 21 | 1.1671 | 0.6275 |
| 1.2512 | 12.57 | 22 | 1.1560 | 0.6078 |
| 1.2512 | 13.71 | 24 | 1.1311 | 0.6471 |
| 1.2512 | 14.86 | 26 | 1.1128 | 0.6275 |
| 1.2512 | 16.0 | 28 | 1.0874 | 0.6667 |
| 1.2512 | 16.57 | 29 | 1.0828 | 0.6863 |
| 1.1299 | 17.71 | 31 | 1.0586 | 0.6667 |
| 1.1299 | 18.86 | 33 | 1.0362 | 0.6667 |
| 1.1299 | 20.0 | 35 | 1.0173 | 0.6863 |
| 1.1299 | 20.57 | 36 | 1.0065 | 0.6667 |
| 1.1299 | 21.71 | 38 | 1.0070 | 0.6471 |
| 1.0212 | 22.86 | 40 | 0.9792 | 0.6667 |
| 1.0212 | 24.0 | 42 | 0.9612 | 0.6667 |
| 1.0212 | 24.57 | 43 | 0.9584 | 0.6471 |
| 1.0212 | 25.71 | 45 | 0.9494 | 0.6667 |
| 1.0212 | 26.86 | 47 | 0.9294 | 0.6667 |
| 1.0212 | 28.0 | 49 | 0.9196 | 0.6667 |
| 0.9222 | 28.57 | 50 | 0.9100 | 0.7059 |
| 0.9222 | 29.71 | 52 | 0.9061 | 0.6863 |
| 0.9222 | 30.86 | 54 | 0.8904 | 0.7059 |
| 0.9222 | 32.0 | 56 | 0.8797 | 0.7059 |
| 0.9222 | 32.57 | 57 | 0.8747 | 0.6863 |
| 0.9222 | 33.71 | 59 | 0.8691 | 0.6863 |
| 0.8419 | 34.86 | 61 | 0.8550 | 0.7059 |
| 0.8419 | 36.0 | 63 | 0.8470 | 0.7255 |
| 0.8419 | 36.57 | 64 | 0.8430 | 0.7255 |
| 0.8419 | 37.71 | 66 | 0.8389 | 0.7059 |
| 0.8419 | 38.86 | 68 | 0.8298 | 0.7255 |
| 0.7865 | 40.0 | 70 | 0.8270 | 0.7255 |
| 0.7865 | 40.57 | 71 | 0.8258 | 0.7255 |
| 0.7865 | 41.71 | 73 | 0.8235 | 0.7059 |
| 0.7865 | 42.86 | 75 | 0.8211 | 0.7059 |
| 0.7865 | 44.0 | 77 | 0.8189 | 0.7059 |
| 0.7865 | 44.57 | 78 | 0.8189 | 0.7059 |
| 0.7555 | 45.71 | 80 | 0.8187 | 0.7059 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
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