ernie-ai/finetuned-vit-image-text-classifier

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