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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: violation-classification-bantai-vit-v80ep
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9559725730783111
---
<!-- 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. -->
# violation-classification-bantai-vit-v80ep
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 image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1974
- Accuracy: 0.9560
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.797 | 4.95 | 500 | 0.3926 | 0.8715 |
| 0.3095 | 9.9 | 1000 | 0.2597 | 0.9107 |
| 0.1726 | 14.85 | 1500 | 0.2157 | 0.9253 |
| 0.1259 | 19.8 | 2000 | 0.1870 | 0.9392 |
| 0.0959 | 24.75 | 2500 | 0.1797 | 0.9444 |
| 0.0835 | 29.7 | 3000 | 0.2293 | 0.9354 |
| 0.0722 | 34.65 | 3500 | 0.1921 | 0.9441 |
| 0.0628 | 39.6 | 4000 | 0.1897 | 0.9491 |
| 0.059 | 44.55 | 4500 | 0.1719 | 0.9520 |
| 0.0531 | 49.5 | 5000 | 0.1987 | 0.9513 |
| 0.046 | 54.45 | 5500 | 0.1713 | 0.9556 |
| 0.0444 | 59.4 | 6000 | 0.2016 | 0.9525 |
| 0.042 | 64.36 | 6500 | 0.1950 | 0.9525 |
| 0.0363 | 69.31 | 7000 | 0.2017 | 0.9549 |
| 0.037 | 74.26 | 7500 | 0.1943 | 0.9551 |
| 0.0343 | 79.21 | 8000 | 0.1974 | 0.9560 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
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