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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vc-bantai-vit-withoutAMBI-adunest-v2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
args: Violation-Classification---Raw-10
metrics:
- name: Accuracy
type: accuracy
value: 0.7705338809034907
---
<!-- 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-v2
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: 0.8271
- Accuracy: 0.7705
## 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 | 0.4 | 100 | 0.3811 | 0.8511 |
| No log | 0.81 | 200 | 0.3707 | 0.8609 |
| No log | 1.21 | 300 | 0.5708 | 0.7325 |
| No log | 1.61 | 400 | 0.3121 | 0.8778 |
| 0.3308 | 2.02 | 500 | 0.3358 | 0.8445 |
| 0.3308 | 2.42 | 600 | 0.2820 | 0.8768 |
| 0.3308 | 2.82 | 700 | 0.4825 | 0.7695 |
| 0.3308 | 3.23 | 800 | 0.3133 | 0.8640 |
| 0.3308 | 3.63 | 900 | 0.4509 | 0.8219 |
| 0.2028 | 4.03 | 1000 | 0.5426 | 0.7551 |
| 0.2028 | 4.44 | 1100 | 0.4886 | 0.8552 |
| 0.2028 | 4.84 | 1200 | 0.5649 | 0.7695 |
| 0.2028 | 5.24 | 1300 | 0.5925 | 0.7900 |
| 0.2028 | 5.65 | 1400 | 0.4203 | 0.8439 |
| 0.1471 | 6.05 | 1500 | 0.4275 | 0.8486 |
| 0.1471 | 6.45 | 1600 | 0.3683 | 0.8727 |
| 0.1471 | 6.85 | 1700 | 0.5709 | 0.8121 |
| 0.1471 | 7.26 | 1800 | 0.6209 | 0.7680 |
| 0.1471 | 7.66 | 1900 | 0.4971 | 0.8147 |
| 0.101 | 8.06 | 2000 | 0.8792 | 0.7567 |
| 0.101 | 8.47 | 2100 | 0.3288 | 0.8670 |
| 0.101 | 8.87 | 2200 | 0.3643 | 0.8342 |
| 0.101 | 9.27 | 2300 | 0.4883 | 0.8711 |
| 0.101 | 9.68 | 2400 | 0.2892 | 0.8943 |
| 0.0667 | 10.08 | 2500 | 0.5437 | 0.8398 |
| 0.0667 | 10.48 | 2600 | 0.5841 | 0.8450 |
| 0.0667 | 10.89 | 2700 | 0.8016 | 0.8219 |
| 0.0667 | 11.29 | 2800 | 0.6389 | 0.7772 |
| 0.0667 | 11.69 | 2900 | 0.3714 | 0.8753 |
| 0.0674 | 12.1 | 3000 | 0.9811 | 0.7130 |
| 0.0674 | 12.5 | 3100 | 0.6359 | 0.8101 |
| 0.0674 | 12.9 | 3200 | 0.5691 | 0.8285 |
| 0.0674 | 13.31 | 3300 | 0.6123 | 0.8316 |
| 0.0674 | 13.71 | 3400 | 0.3655 | 0.8978 |
| 0.0525 | 14.11 | 3500 | 0.4988 | 0.8583 |
| 0.0525 | 14.52 | 3600 | 0.6153 | 0.8450 |
| 0.0525 | 14.92 | 3700 | 0.4189 | 0.8881 |
| 0.0525 | 15.32 | 3800 | 0.9713 | 0.7967 |
| 0.0525 | 15.73 | 3900 | 1.1224 | 0.7967 |
| 0.0438 | 16.13 | 4000 | 0.5725 | 0.8578 |
| 0.0438 | 16.53 | 4100 | 0.4725 | 0.8532 |
| 0.0438 | 16.94 | 4200 | 0.4696 | 0.8640 |
| 0.0438 | 17.34 | 4300 | 0.4028 | 0.8789 |
| 0.0438 | 17.74 | 4400 | 0.9452 | 0.7746 |
| 0.0462 | 18.15 | 4500 | 0.4455 | 0.8783 |
| 0.0462 | 18.55 | 4600 | 0.6328 | 0.8311 |
| 0.0462 | 18.95 | 4700 | 0.6707 | 0.8296 |
| 0.0462 | 19.35 | 4800 | 0.7771 | 0.8429 |
| 0.0462 | 19.76 | 4900 | 1.2832 | 0.7408 |
| 0.0381 | 20.16 | 5000 | 0.5415 | 0.8737 |
| 0.0381 | 20.56 | 5100 | 0.8932 | 0.7977 |
| 0.0381 | 20.97 | 5200 | 0.5182 | 0.8691 |
| 0.0381 | 21.37 | 5300 | 0.5967 | 0.8794 |
| 0.0381 | 21.77 | 5400 | 0.8271 | 0.7705 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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