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
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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 |