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library_name: peft
license: apache-2.0
pipeline_tag: image-classification # 👈 tells the router what to do
base_model: facebook/dinov2-base-imagenet1k-1-layer
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: dinov2-fonts-with-subfonts-3
results: []
---
<!-- 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. -->
# dinov2-fonts-with-subfonts-3
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 |