metadata

license: apache-2.0
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: finetuned-vit-doc-text-classifer
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: ernie-ai/image-text-examples-ar-cn-latin-notext
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9029850746268657

finetuned-vit-doc-text-classifer

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the ernie-ai/image-text-examples-ar-cn-latin-notext dataset. It achieves the following results on the evaluation set:

Loss: 0.3107
Accuracy: 0.9030

Model description

It is an image classificatin model fine-tuned to predict whether an images contains text and if that text is Latin script, Chinese or Arabic. It also classifies non-text images.

Training and evaluation data

Dataset: [ernie-ai/image-text-examples-ar-cn-latin-notext]

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 8
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2719	2.08	100	0.4120	0.8657
0.1027	4.17	200	0.3907	0.8881
0.0723	6.25	300	0.3107	0.9030

Framework versions

Transformers 4.26.0
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2