metadata

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

violation-classification-bantai-vit-v80ep

This model is a fine-tuned version of 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