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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vc-bantai-vit-withoutAMBI-adunest-v3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
args: Violation-Classification---Raw-10
metrics:
- name: Accuracy
type: accuracy
value: 0.8218352310783658
---
<!-- 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. -->
# vc-bantai-vit-withoutAMBI-adunest-v3
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: 0.8889
- Accuracy: 0.8218
## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.38 | 100 | 0.8208 | 0.7147 |
| No log | 0.76 | 200 | 0.8861 | 0.7595 |
| No log | 1.14 | 300 | 0.4306 | 0.7910 |
| No log | 1.52 | 400 | 0.5222 | 0.8245 |
| 0.3448 | 1.9 | 500 | 0.8621 | 0.7602 |
| 0.3448 | 2.28 | 600 | 0.2902 | 0.8801 |
| 0.3448 | 2.66 | 700 | 0.3687 | 0.8426 |
| 0.3448 | 3.04 | 800 | 0.3585 | 0.8694 |
| 0.3448 | 3.42 | 900 | 0.6546 | 0.7897 |
| 0.2183 | 3.8 | 1000 | 0.3881 | 0.8272 |
| 0.2183 | 4.18 | 1100 | 0.9650 | 0.7709 |
| 0.2183 | 4.56 | 1200 | 0.6444 | 0.7917 |
| 0.2183 | 4.94 | 1300 | 0.4685 | 0.8707 |
| 0.2183 | 5.32 | 1400 | 0.4972 | 0.8506 |
| 0.157 | 5.7 | 1500 | 0.4010 | 0.8513 |
| 0.157 | 6.08 | 1600 | 0.4629 | 0.8419 |
| 0.157 | 6.46 | 1700 | 0.4258 | 0.8714 |
| 0.157 | 6.84 | 1800 | 0.4383 | 0.8573 |
| 0.157 | 7.22 | 1900 | 0.5324 | 0.8493 |
| 0.113 | 7.6 | 2000 | 0.3212 | 0.8942 |
| 0.113 | 7.98 | 2100 | 0.8621 | 0.8326 |
| 0.113 | 8.37 | 2200 | 0.6050 | 0.8131 |
| 0.113 | 8.75 | 2300 | 0.7173 | 0.7991 |
| 0.113 | 9.13 | 2400 | 0.5313 | 0.8125 |
| 0.0921 | 9.51 | 2500 | 0.6584 | 0.8158 |
| 0.0921 | 9.89 | 2600 | 0.8727 | 0.7930 |
| 0.0921 | 10.27 | 2700 | 0.4222 | 0.8922 |
| 0.0921 | 10.65 | 2800 | 0.5811 | 0.8265 |
| 0.0921 | 11.03 | 2900 | 0.6175 | 0.8372 |
| 0.0701 | 11.41 | 3000 | 0.3914 | 0.8835 |
| 0.0701 | 11.79 | 3100 | 0.3364 | 0.8654 |
| 0.0701 | 12.17 | 3200 | 0.6223 | 0.8359 |
| 0.0701 | 12.55 | 3300 | 0.7830 | 0.8125 |
| 0.0701 | 12.93 | 3400 | 0.4356 | 0.8942 |
| 0.0552 | 13.31 | 3500 | 0.7553 | 0.8232 |
| 0.0552 | 13.69 | 3600 | 0.9107 | 0.8292 |
| 0.0552 | 14.07 | 3700 | 0.6108 | 0.8580 |
| 0.0552 | 14.45 | 3800 | 0.5732 | 0.8567 |
| 0.0552 | 14.83 | 3900 | 0.5087 | 0.8614 |
| 0.0482 | 15.21 | 4000 | 0.8889 | 0.8218 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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