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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vc-bantai-vit-withoutAMBI-adunest
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
args: Violation-Classification---Raw-6
metrics:
- name: Accuracy
type: accuracy
value: 0.9388646288209607
---
<!-- 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
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: 0.1950
- Accuracy: 0.9389
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4821 | 0.11 | 100 | 0.7644 | 0.6714 |
| 0.7032 | 0.23 | 200 | 0.5568 | 0.75 |
| 0.5262 | 0.34 | 300 | 0.4440 | 0.7806 |
| 0.4719 | 0.45 | 400 | 0.3893 | 0.8144 |
| 0.5021 | 0.57 | 500 | 0.5129 | 0.8090 |
| 0.3123 | 0.68 | 600 | 0.4536 | 0.7980 |
| 0.3606 | 0.79 | 700 | 0.3679 | 0.8483 |
| 0.4081 | 0.91 | 800 | 0.3335 | 0.8559 |
| 0.3624 | 1.02 | 900 | 0.3149 | 0.8592 |
| 0.1903 | 1.14 | 1000 | 0.3296 | 0.8766 |
| 0.334 | 1.25 | 1100 | 0.2832 | 0.8897 |
| 0.2731 | 1.36 | 1200 | 0.2546 | 0.8930 |
| 0.311 | 1.48 | 1300 | 0.2585 | 0.8908 |
| 0.3209 | 1.59 | 1400 | 0.2701 | 0.8854 |
| 0.4005 | 1.7 | 1500 | 0.2643 | 0.8897 |
| 0.3128 | 1.82 | 1600 | 0.2864 | 0.8843 |
| 0.3376 | 1.93 | 1700 | 0.2882 | 0.8657 |
| 0.2698 | 2.04 | 1800 | 0.2876 | 0.9028 |
| 0.2347 | 2.16 | 1900 | 0.2405 | 0.8974 |
| 0.2436 | 2.27 | 2000 | 0.2804 | 0.8886 |
| 0.1764 | 2.38 | 2100 | 0.2852 | 0.8952 |
| 0.1197 | 2.5 | 2200 | 0.2312 | 0.9127 |
| 0.1082 | 2.61 | 2300 | 0.2133 | 0.9116 |
| 0.1245 | 2.72 | 2400 | 0.2677 | 0.8985 |
| 0.1335 | 2.84 | 2500 | 0.2098 | 0.9181 |
| 0.2194 | 2.95 | 2600 | 0.1911 | 0.9127 |
| 0.089 | 3.06 | 2700 | 0.2062 | 0.9181 |
| 0.0465 | 3.18 | 2800 | 0.2414 | 0.9247 |
| 0.0985 | 3.29 | 2900 | 0.1869 | 0.9389 |
| 0.1113 | 3.41 | 3000 | 0.1819 | 0.9323 |
| 0.1392 | 3.52 | 3100 | 0.2101 | 0.9312 |
| 0.0621 | 3.63 | 3200 | 0.2201 | 0.9367 |
| 0.1168 | 3.75 | 3300 | 0.1935 | 0.9389 |
| 0.059 | 3.86 | 3400 | 0.1946 | 0.9367 |
| 0.0513 | 3.97 | 3500 | 0.1950 | 0.9389 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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