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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-RU9-24
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-RU9-24
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: 0.5081
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 8 | 1.3401 | 0.5098 |
| 1.3685 | 2.0 | 16 | 1.2193 | 0.5686 |
| 1.2413 | 3.0 | 24 | 1.1150 | 0.5882 |
| 1.1126 | 4.0 | 32 | 0.9957 | 0.7059 |
| 0.9285 | 5.0 | 40 | 0.8976 | 0.6863 |
| 0.9285 | 6.0 | 48 | 0.8580 | 0.6863 |
| 0.7793 | 7.0 | 56 | 0.8426 | 0.7647 |
| 0.6291 | 8.0 | 64 | 0.7899 | 0.6863 |
| 0.5401 | 9.0 | 72 | 0.7169 | 0.7255 |
| 0.4358 | 10.0 | 80 | 0.7505 | 0.7255 |
| 0.4358 | 11.0 | 88 | 0.8077 | 0.7059 |
| 0.3901 | 12.0 | 96 | 0.6803 | 0.7647 |
| 0.3033 | 13.0 | 104 | 0.6483 | 0.7647 |
| 0.267 | 14.0 | 112 | 0.6451 | 0.7451 |
| 0.2212 | 15.0 | 120 | 0.6119 | 0.7647 |
| 0.2212 | 16.0 | 128 | 0.6150 | 0.8039 |
| 0.2206 | 17.0 | 136 | 0.6270 | 0.7843 |
| 0.2285 | 18.0 | 144 | 0.6181 | 0.7647 |
| 0.1741 | 19.0 | 152 | 0.5081 | 0.8431 |
| 0.1708 | 20.0 | 160 | 0.5502 | 0.8235 |
| 0.1708 | 21.0 | 168 | 0.5689 | 0.8039 |
| 0.16 | 22.0 | 176 | 0.5137 | 0.8235 |
| 0.1567 | 23.0 | 184 | 0.5207 | 0.8431 |
| 0.1616 | 24.0 | 192 | 0.5375 | 0.8235 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
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