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	---
	license: apache-2.0
	pipeline_tag: image-classification
	library_name: transformers # ← change “peft” → “transformers”
	tags:
	- dinov2
	- image-classification
	- fonts
	---

	# 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. -->

	This model is a fine-tuned version of [facebook/dinov2-base-imagenet1k-1-layer](https://huggingface.co/facebook/dinov2-base-imagenet1k-1-layer) on the imagefolder dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.2637
	- Model Preparation Time: 0.0016
	- Accuracy: 0.9163

	## 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: 0.0001
	- 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
	- num_epochs: 1

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \| Accuracy \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|:----------------------:\|:--------:\|
	\| 0.7099 \| 0.0182 \| 50 \| 0.6595 \| 0.0016 \| 0.7594 \|
	\| 0.7084 \| 0.0363 \| 100 \| 0.6175 \| 0.0016 \| 0.7806 \|
	\| 0.7638 \| 0.0545 \| 150 \| 0.7014 \| 0.0016 \| 0.7337 \|
	\| 0.6451 \| 0.0727 \| 200 \| 0.6177 \| 0.0016 \| 0.7757 \|
	\| 0.6852 \| 0.0908 \| 250 \| 0.5691 \| 0.0016 \| 0.7971 \|
	\| 0.5753 \| 0.1090 \| 300 \| 0.5666 \| 0.0016 \| 0.8048 \|
	\| 0.5925 \| 0.1272 \| 350 \| 0.5235 \| 0.0016 \| 0.8204 \|
	\| 0.6969 \| 0.1453 \| 400 \| 0.5725 \| 0.0016 \| 0.7922 \|
	\| 0.6096 \| 0.1635 \| 450 \| 0.5103 \| 0.0016 \| 0.8173 \|
	\| 0.5994 \| 0.1817 \| 500 \| 0.5075 \| 0.0016 \| 0.8183 \|
	\| 0.5272 \| 0.1999 \| 550 \| 0.5116 \| 0.0016 \| 0.8229 \|
	\| 0.5193 \| 0.2180 \| 600 \| 0.4952 \| 0.0016 \| 0.8244 \|
	\| 0.5689 \| 0.2362 \| 650 \| 0.4662 \| 0.0016 \| 0.8388 \|
	\| 0.5126 \| 0.2544 \| 700 \| 0.4651 \| 0.0016 \| 0.8327 \|
	\| 0.5301 \| 0.2725 \| 750 \| 0.5080 \| 0.0016 \| 0.8158 \|
	\| 0.5424 \| 0.2907 \| 800 \| 0.4573 \| 0.0016 \| 0.8357 \|
	\| 0.4357 \| 0.3089 \| 850 \| 0.4412 \| 0.0016 \| 0.8486 \|
	\| 0.5522 \| 0.3270 \| 900 \| 0.4755 \| 0.0016 \| 0.8256 \|
	\| 0.5639 \| 0.3452 \| 950 \| 0.4463 \| 0.0016 \| 0.8339 \|
	\| 0.4522 \| 0.3634 \| 1000 \| 0.4347 \| 0.0016 \| 0.8458 \|
	\| 0.5548 \| 0.3815 \| 1050 \| 0.4112 \| 0.0016 \| 0.8560 \|
	\| 0.4815 \| 0.3997 \| 1100 \| 0.4300 \| 0.0016 \| 0.8514 \|
	\| 0.5028 \| 0.4179 \| 1150 \| 0.3840 \| 0.0016 \| 0.8713 \|
	\| 0.4417 \| 0.4360 \| 1200 \| 0.4364 \| 0.0016 \| 0.8462 \|
	\| 0.4465 \| 0.4542 \| 1250 \| 0.3731 \| 0.0016 \| 0.8740 \|
	\| 0.3935 \| 0.4724 \| 1300 \| 0.3672 \| 0.0016 \| 0.8753 \|
	\| 0.5306 \| 0.4906 \| 1350 \| 0.4480 \| 0.0016 \| 0.8388 \|
	\| 0.3991 \| 0.5087 \| 1400 \| 0.3718 \| 0.0016 \| 0.8698 \|
	\| 0.483 \| 0.5269 \| 1450 \| 0.3916 \| 0.0016 \| 0.8652 \|
	\| 0.4323 \| 0.5451 \| 1500 \| 0.3948 \| 0.0016 \| 0.8648 \|
	\| 0.3664 \| 0.5632 \| 1550 \| 0.3400 \| 0.0016 \| 0.8796 \|
	\| 0.4941 \| 0.5814 \| 1600 \| 0.3531 \| 0.0016 \| 0.8765 \|
	\| 0.4185 \| 0.5996 \| 1650 \| 0.3481 \| 0.0016 \| 0.8820 \|
	\| 0.4506 \| 0.6177 \| 1700 \| 0.3332 \| 0.0016 \| 0.8866 \|
	\| 0.4015 \| 0.6359 \| 1750 \| 0.3468 \| 0.0016 \| 0.8768 \|
	\| 0.3919 \| 0.6541 \| 1800 \| 0.3421 \| 0.0016 \| 0.8897 \|
	\| 0.4281 \| 0.6722 \| 1850 \| 0.3141 \| 0.0016 \| 0.8937 \|
	\| 0.3659 \| 0.6904 \| 1900 \| 0.3424 \| 0.0016 \| 0.8823 \|
	\| 0.345 \| 0.7086 \| 1950 \| 0.3172 \| 0.0016 \| 0.8912 \|
	\| 0.3157 \| 0.7267 \| 2000 \| 0.3226 \| 0.0016 \| 0.8903 \|
	\| 0.3456 \| 0.7449 \| 2050 \| 0.3178 \| 0.0016 \| 0.8909 \|
	\| 0.3643 \| 0.7631 \| 2100 \| 0.2988 \| 0.0016 \| 0.8983 \|
	\| 0.4043 \| 0.7812 \| 2150 \| 0.3036 \| 0.0016 \| 0.8992 \|
	\| 0.3486 \| 0.7994 \| 2200 \| 0.2974 \| 0.0016 \| 0.9053 \|
	\| 0.3735 \| 0.8176 \| 2250 \| 0.3026 \| 0.0016 \| 0.8964 \|
	\| 0.4032 \| 0.8358 \| 2300 \| 0.2990 \| 0.0016 \| 0.9019 \|
	\| 0.3825 \| 0.8539 \| 2350 \| 0.2938 \| 0.0016 \| 0.9062 \|
	\| 0.345 \| 0.8721 \| 2400 \| 0.2871 \| 0.0016 \| 0.9059 \|
	\| 0.3528 \| 0.8903 \| 2450 \| 0.2777 \| 0.0016 \| 0.9093 \|
	\| 0.3207 \| 0.9084 \| 2500 \| 0.2764 \| 0.0016 \| 0.9111 \|
	\| 0.2664 \| 0.9266 \| 2550 \| 0.2741 \| 0.0016 \| 0.9099 \|
	\| 0.3496 \| 0.9448 \| 2600 \| 0.2720 \| 0.0016 \| 0.9151 \|
	\| 0.3274 \| 0.9629 \| 2650 \| 0.2724 \| 0.0016 \| 0.9136 \|
	\| 0.3014 \| 0.9811 \| 2700 \| 0.2659 \| 0.0016 \| 0.9136 \|
	\| 0.3235 \| 0.9993 \| 2750 \| 0.2637 \| 0.0016 \| 0.9163 \|


	### Framework versions

	- PEFT 0.15.2
	- Transformers 4.52.4
	- Pytorch 2.7.1
	- Datasets 3.6.0
	- Tokenizers 0.21.1

	---
	license: apache-2.0
	pipeline_tag: image-classification
	library_name: transformers # ← change “peft” → “transformers”
	tags:
	- dinov2
	- image-classification
	- fonts
	---

	# 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. -->

	This model is a fine-tuned version of [facebook/dinov2-base-imagenet1k-1-layer](https://huggingface.co/facebook/dinov2-base-imagenet1k-1-layer) on the imagefolder dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.2637
	- Model Preparation Time: 0.0016
	- Accuracy: 0.9163

	## 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: 0.0001
	- 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
	- num_epochs: 1

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \| Accuracy \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|:----------------------:\|:--------:\|
	\| 0.7099 \| 0.0182 \| 50 \| 0.6595 \| 0.0016 \| 0.7594 \|
	\| 0.7084 \| 0.0363 \| 100 \| 0.6175 \| 0.0016 \| 0.7806 \|
	\| 0.7638 \| 0.0545 \| 150 \| 0.7014 \| 0.0016 \| 0.7337 \|
	\| 0.6451 \| 0.0727 \| 200 \| 0.6177 \| 0.0016 \| 0.7757 \|
	\| 0.6852 \| 0.0908 \| 250 \| 0.5691 \| 0.0016 \| 0.7971 \|
	\| 0.5753 \| 0.1090 \| 300 \| 0.5666 \| 0.0016 \| 0.8048 \|
	\| 0.5925 \| 0.1272 \| 350 \| 0.5235 \| 0.0016 \| 0.8204 \|
	\| 0.6969 \| 0.1453 \| 400 \| 0.5725 \| 0.0016 \| 0.7922 \|
	\| 0.6096 \| 0.1635 \| 450 \| 0.5103 \| 0.0016 \| 0.8173 \|
	\| 0.5994 \| 0.1817 \| 500 \| 0.5075 \| 0.0016 \| 0.8183 \|
	\| 0.5272 \| 0.1999 \| 550 \| 0.5116 \| 0.0016 \| 0.8229 \|
	\| 0.5193 \| 0.2180 \| 600 \| 0.4952 \| 0.0016 \| 0.8244 \|
	\| 0.5689 \| 0.2362 \| 650 \| 0.4662 \| 0.0016 \| 0.8388 \|
	\| 0.5126 \| 0.2544 \| 700 \| 0.4651 \| 0.0016 \| 0.8327 \|
	\| 0.5301 \| 0.2725 \| 750 \| 0.5080 \| 0.0016 \| 0.8158 \|
	\| 0.5424 \| 0.2907 \| 800 \| 0.4573 \| 0.0016 \| 0.8357 \|
	\| 0.4357 \| 0.3089 \| 850 \| 0.4412 \| 0.0016 \| 0.8486 \|
	\| 0.5522 \| 0.3270 \| 900 \| 0.4755 \| 0.0016 \| 0.8256 \|
	\| 0.5639 \| 0.3452 \| 950 \| 0.4463 \| 0.0016 \| 0.8339 \|
	\| 0.4522 \| 0.3634 \| 1000 \| 0.4347 \| 0.0016 \| 0.8458 \|
	\| 0.5548 \| 0.3815 \| 1050 \| 0.4112 \| 0.0016 \| 0.8560 \|
	\| 0.4815 \| 0.3997 \| 1100 \| 0.4300 \| 0.0016 \| 0.8514 \|
	\| 0.5028 \| 0.4179 \| 1150 \| 0.3840 \| 0.0016 \| 0.8713 \|
	\| 0.4417 \| 0.4360 \| 1200 \| 0.4364 \| 0.0016 \| 0.8462 \|
	\| 0.4465 \| 0.4542 \| 1250 \| 0.3731 \| 0.0016 \| 0.8740 \|
	\| 0.3935 \| 0.4724 \| 1300 \| 0.3672 \| 0.0016 \| 0.8753 \|
	\| 0.5306 \| 0.4906 \| 1350 \| 0.4480 \| 0.0016 \| 0.8388 \|
	\| 0.3991 \| 0.5087 \| 1400 \| 0.3718 \| 0.0016 \| 0.8698 \|
	\| 0.483 \| 0.5269 \| 1450 \| 0.3916 \| 0.0016 \| 0.8652 \|
	\| 0.4323 \| 0.5451 \| 1500 \| 0.3948 \| 0.0016 \| 0.8648 \|
	\| 0.3664 \| 0.5632 \| 1550 \| 0.3400 \| 0.0016 \| 0.8796 \|
	\| 0.4941 \| 0.5814 \| 1600 \| 0.3531 \| 0.0016 \| 0.8765 \|
	\| 0.4185 \| 0.5996 \| 1650 \| 0.3481 \| 0.0016 \| 0.8820 \|
	\| 0.4506 \| 0.6177 \| 1700 \| 0.3332 \| 0.0016 \| 0.8866 \|
	\| 0.4015 \| 0.6359 \| 1750 \| 0.3468 \| 0.0016 \| 0.8768 \|
	\| 0.3919 \| 0.6541 \| 1800 \| 0.3421 \| 0.0016 \| 0.8897 \|
	\| 0.4281 \| 0.6722 \| 1850 \| 0.3141 \| 0.0016 \| 0.8937 \|
	\| 0.3659 \| 0.6904 \| 1900 \| 0.3424 \| 0.0016 \| 0.8823 \|
	\| 0.345 \| 0.7086 \| 1950 \| 0.3172 \| 0.0016 \| 0.8912 \|
	\| 0.3157 \| 0.7267 \| 2000 \| 0.3226 \| 0.0016 \| 0.8903 \|
	\| 0.3456 \| 0.7449 \| 2050 \| 0.3178 \| 0.0016 \| 0.8909 \|
	\| 0.3643 \| 0.7631 \| 2100 \| 0.2988 \| 0.0016 \| 0.8983 \|
	\| 0.4043 \| 0.7812 \| 2150 \| 0.3036 \| 0.0016 \| 0.8992 \|
	\| 0.3486 \| 0.7994 \| 2200 \| 0.2974 \| 0.0016 \| 0.9053 \|
	\| 0.3735 \| 0.8176 \| 2250 \| 0.3026 \| 0.0016 \| 0.8964 \|
	\| 0.4032 \| 0.8358 \| 2300 \| 0.2990 \| 0.0016 \| 0.9019 \|
	\| 0.3825 \| 0.8539 \| 2350 \| 0.2938 \| 0.0016 \| 0.9062 \|
	\| 0.345 \| 0.8721 \| 2400 \| 0.2871 \| 0.0016 \| 0.9059 \|
	\| 0.3528 \| 0.8903 \| 2450 \| 0.2777 \| 0.0016 \| 0.9093 \|
	\| 0.3207 \| 0.9084 \| 2500 \| 0.2764 \| 0.0016 \| 0.9111 \|
	\| 0.2664 \| 0.9266 \| 2550 \| 0.2741 \| 0.0016 \| 0.9099 \|
	\| 0.3496 \| 0.9448 \| 2600 \| 0.2720 \| 0.0016 \| 0.9151 \|
	\| 0.3274 \| 0.9629 \| 2650 \| 0.2724 \| 0.0016 \| 0.9136 \|
	\| 0.3014 \| 0.9811 \| 2700 \| 0.2659 \| 0.0016 \| 0.9136 \|
	\| 0.3235 \| 0.9993 \| 2750 \| 0.2637 \| 0.0016 \| 0.9163 \|


	### Framework versions

	- PEFT 0.15.2
	- Transformers 4.52.4
	- Pytorch 2.7.1
	- Datasets 3.6.0
	- Tokenizers 0.21.1