Instructions to use dchen0/font_classifier_v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dchen0/font_classifier_v4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dchen0/font_classifier_v4") 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("dchen0/font_classifier_v4") model = AutoModelForImageClassification.from_pretrained("dchen0/font_classifier_v4") - Notebooks
- Google Colab
- Kaggle
| base_model: | |
| - facebook/dinov2-base | |
| pipeline_tag: image-classification | |
| license: mit | |
| language: | |
| - en | |
| library_name: transformers | |
| tags: | |
| - dinov2 | |
| # dchen0/font-classifier | |
| Merged DINOv2‑base checkpoint with LoRA weights for font classification. | |
| <!-- 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. --> | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| -learning_rate 1e-4 | |
| -lora_rank 8 | |
| -lora_alpha 16 | |
| -lora_dropout 0.1 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 32 | |
| - 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 | |
| ### Framework versions | |
| - PEFT 0.15.2 | |
| - Transformers 4.52.4 | |
| - Pytorch 2.7.1 | |
| - Datasets 3.6.0 | |
| - Tokenizers 0.21.1 |