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
- Downloads last month
- 16
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Evaluation results
- Accuracy on ernie-ai/image-text-examples-ar-cn-latin-notextself-reported0.903