# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("dchen0/font-classifier-v3")
model = AutoModelForImageClassification.from_pretrained("dchen0/font-classifier-v3")Quick Links
dchen0/font-classifier
Merged DINOv2‑base checkpoint with LoRA weights for font classification.
This model is a fine-tuned version of facebook/dinov2-base-imagenet1k-1-layer on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.3446824550628662
- Accuracy: 0.5903
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
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Model tree for dchen0/font-classifier-v3
Base model
facebook/dinov2-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dchen0/font-classifier-v3") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")