Instructions to use Dugerij/dummy_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dugerij/dummy_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dugerij/dummy_classification_model") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Dugerij/dummy_classification_model") model = AutoModelForImageClassification.from_pretrained("Dugerij/dummy_classification_model") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: google/vit-base-patch16-224 | |
| tags: | |
| - image-classification | |
| - vision | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: dummy_classification_model | |
| results: [] | |
| <!-- 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. --> | |
| # dummy_classification_model | |
| This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the taresco/newspaper_ocr dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0136 | |
| - Accuracy: 0.9969 | |
| ## 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: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3 | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.51.3 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 3.5.0 | |
| - Tokenizers 0.21.0 | |