# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("Dugerij/dummy_classification_model")
model = AutoModelForImageClassification.from_pretrained("Dugerij/dummy_classification_model")Quick Links
dummy_classification_model
This model is a fine-tuned version of 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
- Downloads last month
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Model tree for Dugerij/dummy_classification_model
Base model
google/vit-base-patch16-224
# 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")