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library_name: transformers
license: apache-2.0
base_model: microsoft/resnet-18
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: font-identifier
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9162162162162162
---
<!-- 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. -->
# font-identifier
This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9162
- Loss: 0.2760
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 3.7676 | 1.0 | 24 | 0.0459 | 3.6832 |
| 3.607 | 2.0 | 48 | 0.0919 | 3.4400 |
| 3.2234 | 3.0 | 72 | 0.1919 | 3.0452 |
| 2.8944 | 4.0 | 96 | 0.3324 | 2.5182 |
| 2.1637 | 5.0 | 120 | 0.4351 | 2.0193 |
| 1.9347 | 6.0 | 144 | 0.5595 | 1.6222 |
| 1.6851 | 7.0 | 168 | 0.6297 | 1.3065 |
| 1.369 | 8.0 | 192 | 0.6919 | 1.0945 |
| 1.2987 | 9.0 | 216 | 0.7270 | 0.9188 |
| 1.1044 | 10.0 | 240 | 0.7541 | 0.8216 |
| 1.044 | 11.0 | 264 | 0.8 | 0.7295 |
| 1.0134 | 12.0 | 288 | 0.8270 | 0.6655 |
| 0.9284 | 13.0 | 312 | 0.8189 | 0.6212 |
| 0.8603 | 14.0 | 336 | 0.8216 | 0.5687 |
| 0.7748 | 15.0 | 360 | 0.8649 | 0.5291 |
| 0.8133 | 16.0 | 384 | 0.8324 | 0.5337 |
| 0.8379 | 17.0 | 408 | 0.8486 | 0.4993 |
| 0.751 | 18.0 | 432 | 0.8514 | 0.4632 |
| 0.8585 | 19.0 | 456 | 0.8162 | 0.4908 |
| 0.6627 | 20.0 | 480 | 0.8622 | 0.4358 |
| 0.6497 | 21.0 | 504 | 0.8486 | 0.4240 |
| 0.6422 | 22.0 | 528 | 0.8486 | 0.4143 |
| 0.5964 | 23.0 | 552 | 0.8676 | 0.3912 |
| 0.5793 | 24.0 | 576 | 0.8568 | 0.4026 |
| 0.5909 | 25.0 | 600 | 0.8838 | 0.3531 |
| 0.593 | 26.0 | 624 | 0.8811 | 0.3661 |
| 0.5957 | 27.0 | 648 | 0.8892 | 0.3674 |
| 0.5869 | 28.0 | 672 | 0.8892 | 0.3710 |
| 0.4999 | 29.0 | 696 | 0.8919 | 0.3422 |
| 0.4843 | 30.0 | 720 | 0.8946 | 0.3178 |
| 0.5352 | 31.0 | 744 | 0.8865 | 0.3129 |
| 0.4937 | 32.0 | 768 | 0.8973 | 0.3399 |
| 0.483 | 33.0 | 792 | 0.8973 | 0.2855 |
| 0.4265 | 34.0 | 816 | 0.9 | 0.3316 |
| 0.4412 | 35.0 | 840 | 0.8865 | 0.3273 |
| 0.4324 | 36.0 | 864 | 0.8973 | 0.3167 |
| 0.4681 | 37.0 | 888 | 0.9270 | 0.2944 |
| 0.4813 | 38.0 | 912 | 0.9135 | 0.2943 |
| 0.4585 | 39.0 | 936 | 0.9027 | 0.3019 |
| 0.4151 | 40.0 | 960 | 0.8892 | 0.3399 |
| 0.4351 | 41.0 | 984 | 0.9081 | 0.2623 |
| 0.4364 | 42.0 | 1008 | 0.9135 | 0.2892 |
| 0.4632 | 43.0 | 1032 | 0.9081 | 0.3086 |
| 0.3867 | 44.0 | 1056 | 0.9 | 0.2913 |
| 0.4007 | 45.0 | 1080 | 0.9135 | 0.2502 |
| 0.3848 | 46.0 | 1104 | 0.9162 | 0.2702 |
| 0.4061 | 47.0 | 1128 | 0.9162 | 0.2634 |
| 0.3901 | 48.0 | 1152 | 0.9054 | 0.2975 |
| 0.3794 | 49.0 | 1176 | 0.8973 | 0.2590 |
| 0.3583 | 50.0 | 1200 | 0.9162 | 0.2760 |
### Framework versions
- Transformers 4.53.3
- Pytorch 2.7.1
- Datasets 4.0.0
- Tokenizers 0.21.4
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