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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-RXL1-24
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: 0.8431372549019608
---
<!-- 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-RXL1-24
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: 0.6158
- Accuracy: 0.8431
## 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: 5.5e-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: 0.05
- num_epochs: 24
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3745 | 0.95 | 13 | 1.3056 | 0.4706 |
| 1.2896 | 1.96 | 27 | 1.1039 | 0.6471 |
| 0.9896 | 2.98 | 41 | 0.9413 | 0.6471 |
| 0.8472 | 4.0 | 55 | 0.9059 | 0.6275 |
| 0.7375 | 4.95 | 68 | 0.6520 | 0.8039 |
| 0.458 | 5.96 | 82 | 0.6754 | 0.8039 |
| 0.3807 | 6.98 | 96 | 0.6158 | 0.8431 |
| 0.3003 | 8.0 | 110 | 0.5666 | 0.8039 |
| 0.2337 | 8.95 | 123 | 0.5409 | 0.8039 |
| 0.2252 | 9.96 | 137 | 0.7382 | 0.7647 |
| 0.1644 | 10.98 | 151 | 0.6363 | 0.8039 |
| 0.1608 | 12.0 | 165 | 0.6941 | 0.8039 |
| 0.1354 | 12.95 | 178 | 0.6985 | 0.7843 |
| 0.1298 | 13.96 | 192 | 0.6610 | 0.8039 |
| 0.1333 | 14.98 | 206 | 0.6751 | 0.8039 |
| 0.1209 | 16.0 | 220 | 0.7723 | 0.7843 |
| 0.1057 | 16.95 | 233 | 0.8038 | 0.7255 |
| 0.0972 | 17.96 | 247 | 0.8375 | 0.7647 |
| 0.0789 | 18.98 | 261 | 0.6971 | 0.8235 |
| 0.0833 | 20.0 | 275 | 0.7507 | 0.7843 |
| 0.0813 | 20.95 | 288 | 0.7085 | 0.7843 |
| 0.0803 | 21.96 | 302 | 0.7566 | 0.7647 |
| 0.0693 | 22.69 | 312 | 0.7772 | 0.7647 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
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