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---
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: 0.5875
---
<!-- 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: 1.2378
- Accuracy: 0.5875
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 2.0656 | 0.125 |
| No log | 2.0 | 80 | 2.0558 | 0.1938 |
| No log | 3.0 | 120 | 2.0177 | 0.2375 |
| No log | 4.0 | 160 | 1.9156 | 0.3438 |
| No log | 5.0 | 200 | 1.7849 | 0.3063 |
| No log | 6.0 | 240 | 1.6961 | 0.3187 |
| No log | 7.0 | 280 | 1.6026 | 0.3937 |
| No log | 8.0 | 320 | 1.5455 | 0.3688 |
| No log | 9.0 | 360 | 1.4723 | 0.4562 |
| No log | 10.0 | 400 | 1.3931 | 0.5 |
| No log | 11.0 | 440 | 1.4418 | 0.4375 |
| No log | 12.0 | 480 | 1.3306 | 0.4437 |
| 1.5855 | 13.0 | 520 | 1.2437 | 0.575 |
| 1.5855 | 14.0 | 560 | 1.3712 | 0.4875 |
| 1.5855 | 15.0 | 600 | 1.2102 | 0.55 |
| 1.5855 | 16.0 | 640 | 1.3217 | 0.5188 |
| 1.5855 | 17.0 | 680 | 1.3656 | 0.4938 |
| 1.5855 | 18.0 | 720 | 1.3261 | 0.525 |
| 1.5855 | 19.0 | 760 | 1.5611 | 0.4625 |
| 1.5855 | 20.0 | 800 | 1.4503 | 0.5125 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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