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---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: image_classification
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: 0.55
---
<!-- 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. -->
# image_classification
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: 1.3640
- Accuracy: 0.55
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1309 | 1.0 | 20 | 1.3481 | 0.4938 |
| 1.0746 | 2.0 | 40 | 1.3706 | 0.475 |
| 1.0367 | 3.0 | 60 | 1.3161 | 0.5375 |
| 0.9814 | 4.0 | 80 | 1.3837 | 0.45 |
| 0.886 | 5.0 | 100 | 1.3633 | 0.4875 |
| 0.8096 | 6.0 | 120 | 1.3045 | 0.5125 |
| 0.7669 | 7.0 | 140 | 1.3903 | 0.4938 |
| 0.708 | 8.0 | 160 | 1.2867 | 0.5125 |
| 0.6265 | 9.0 | 180 | 1.2244 | 0.5625 |
| 0.6191 | 10.0 | 200 | 1.3461 | 0.525 |
| 0.5598 | 11.0 | 220 | 1.3266 | 0.5625 |
| 0.4667 | 12.0 | 240 | 1.3050 | 0.5563 |
| 0.4613 | 13.0 | 260 | 1.3329 | 0.5375 |
| 0.4268 | 14.0 | 280 | 1.4020 | 0.5312 |
| 0.4256 | 15.0 | 300 | 1.3770 | 0.5188 |
| 0.3727 | 16.0 | 320 | 1.3655 | 0.5188 |
| 0.316 | 17.0 | 340 | 1.3642 | 0.5188 |
| 0.3223 | 18.0 | 360 | 1.2535 | 0.5938 |
| 0.3064 | 19.0 | 380 | 1.4173 | 0.4875 |
| 0.2866 | 20.0 | 400 | 1.3343 | 0.5625 |
| 0.2781 | 21.0 | 420 | 1.5072 | 0.4813 |
| 0.3027 | 22.0 | 440 | 1.5067 | 0.5125 |
| 0.26 | 23.0 | 460 | 1.4456 | 0.5687 |
| 0.2156 | 24.0 | 480 | 1.4825 | 0.525 |
| 0.1908 | 25.0 | 500 | 1.5369 | 0.5375 |
| 0.213 | 26.0 | 520 | 1.5397 | 0.5188 |
| 0.241 | 27.0 | 540 | 1.4804 | 0.5125 |
| 0.1974 | 28.0 | 560 | 1.5786 | 0.5062 |
| 0.225 | 29.0 | 580 | 1.4677 | 0.5375 |
| 0.2459 | 30.0 | 600 | 1.5392 | 0.5312 |
| 0.2146 | 31.0 | 620 | 1.6734 | 0.4625 |
| 0.1891 | 32.0 | 640 | 1.5012 | 0.55 |
| 0.2231 | 33.0 | 660 | 1.6265 | 0.5 |
| 0.1903 | 34.0 | 680 | 1.5405 | 0.5312 |
| 0.1852 | 35.0 | 700 | 1.6295 | 0.5 |
| 0.1768 | 36.0 | 720 | 1.5758 | 0.5375 |
| 0.1486 | 37.0 | 740 | 1.6176 | 0.5188 |
| 0.1814 | 38.0 | 760 | 1.5107 | 0.5375 |
| 0.1642 | 39.0 | 780 | 1.5315 | 0.55 |
| 0.1822 | 40.0 | 800 | 1.6309 | 0.525 |
| 0.1819 | 41.0 | 820 | 1.7033 | 0.4938 |
| 0.1326 | 42.0 | 840 | 1.6107 | 0.5437 |
| 0.1452 | 43.0 | 860 | 1.6219 | 0.55 |
| 0.128 | 44.0 | 880 | 1.4348 | 0.5813 |
| 0.1103 | 45.0 | 900 | 1.6185 | 0.5687 |
| 0.1386 | 46.0 | 920 | 1.5848 | 0.5312 |
| 0.1021 | 47.0 | 940 | 1.6036 | 0.5563 |
| 0.1414 | 48.0 | 960 | 1.5455 | 0.575 |
| 0.1989 | 49.0 | 980 | 1.5955 | 0.525 |
| 0.1458 | 50.0 | 1000 | 1.5511 | 0.55 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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