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---
license: mit
base_model: microsoft/resnet-18
tags:
- generated_from_trainer
datasets:
- gaborcselle/font-examples
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: 0.963265306122449
widget:
- text: What font is this?
- src: >-
https://huggingface.co/gaborcselle/font-identifier/resolve/main/hf_samples/ArchitectsDaughter-Regular_1.png
example_title: Architects Daughter
- src: >-
https://huggingface.co/gaborcselle/font-identifier/resolve/main/hf_samples/Arial%20Bold_39.png
example_title: Arial Bold
- src: >-
https://huggingface.co/gaborcselle/font-identifier/resolve/main/hf_samples/Courier_28.png
example_title: Courier
- src: >-
https://huggingface.co/gaborcselle/font-identifier/resolve/main/hf_samples/Helvetica_3.png
example_title: Helvetica
- src: >-
https://huggingface.co/gaborcselle/font-identifier/resolve/main/hf_samples/IBMPlexSans-Regular_25.png
example_title: IBM Plex Sans
- src: >-
https://huggingface.co/gaborcselle/font-identifier/resolve/main/hf_samples/Inter-Regular_43.png
example_title: Inter
- src: >-
https://huggingface.co/gaborcselle/font-identifier/resolve/main/hf_samples/Lobster-Regular_25.png
example_title: Lobster
- src: >-
https://huggingface.co/gaborcselle/font-identifier/resolve/main/hf_samples/Merriweather-Regular_1.png
example_title: Merriweather
- src: >-
https://huggingface.co/gaborcselle/font-identifier/resolve/main/hf_samples/Poppins-Regular_22.png
example_title: Poppins
- src: >-
https://huggingface.co/gaborcselle/font-identifier/resolve/main/hf_samples/RobotoMono-Regular_38.png
example_title: Roboto Mono
- src: >-
https://huggingface.co/gaborcselle/font-identifier/resolve/main/hf_samples/Times_New_Roman_Bold
Italic_26.png
example_title: Times New Roman Bold Italic
- src: >-
https://huggingface.co/gaborcselle/font-identifier/resolve/main/hf_samples/Times_New_Roman_Italic_16.png
example_title: Times New Roman Italic
- src: >-
https://huggingface.co/gaborcselle/font-identifier/resolve/main/hf_samples/TitilliumWeb-Regular_5.png
example_title: Titillium Web
- src: >-
https://huggingface.co/gaborcselle/font-identifier/resolve/main/hf_samples/Trebuchet_MS_Italic_47.png
example_title: Trebuchet MS Italic
- src: >-
https://huggingface.co/gaborcselle/font-identifier/resolve/main/hf_samples/Trebuchet_MS_11.png
example_title: Trebuchet MS
- src: >-
https://huggingface.co/gaborcselle/font-identifier/resolve/main/hf_samples/Verdana_Bold_43.png
example_title: Verdana Bold
language:
- en
---
# 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:
- Loss: 0.1172
- Accuracy: 0.9633
## Model description
Identify the font used in an image. Visual classifier based on ResNet18.
I built this project in 1 day, with a minute-by-minute journal:
* [On Twitter/X](https://twitter.com/gabor/status/1722300841691103467)
* [On Pebble.social](https://pebble.social/@gabor/111376050835874755)
* [On Threads.net](https://www.threads.net/@gaborcselle/post/CzZJpJCpxTz)
## Intended uses & limitations
Identify any of 48 standard fonts from the training data.
## Training and evaluation data
Trained and eval'd on the [gaborcselle/font-examples](https://huggingface.co/datasets/gaborcselle/font-examples) dataset (80/20 split).
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.0243 | 0.98 | 30 | 3.9884 | 0.0204 |
| 3.7051 | 1.98 | 61 | 3.6012 | 0.0776 |
| 3.2036 | 2.99 | 92 | 2.9556 | 0.2939 |
| 2.6413 | 4.0 | 123 | 2.3054 | 0.4531 |
| 2.1015 | 4.98 | 153 | 1.7366 | 0.5224 |
| 1.6508 | 5.98 | 184 | 1.3509 | 0.6367 |
| 1.3986 | 6.99 | 215 | 1.0938 | 0.7163 |
| 1.1918 | 8.0 | 246 | 0.9012 | 0.7735 |
| 1.0633 | 8.98 | 276 | 0.7464 | 0.8143 |
| 0.8771 | 9.98 | 307 | 0.6569 | 0.8306 |
| 0.8309 | 10.99 | 338 | 0.5536 | 0.8551 |
| 0.7093 | 12.0 | 369 | 0.4795 | 0.8796 |
| 0.6579 | 12.98 | 399 | 0.4176 | 0.8837 |
| 0.5827 | 13.98 | 430 | 0.3888 | 0.8980 |
| 0.5418 | 14.99 | 461 | 0.3255 | 0.9122 |
| 0.5102 | 16.0 | 492 | 0.3139 | 0.9265 |
| 0.472 | 16.98 | 522 | 0.3141 | 0.9163 |
| 0.4273 | 17.98 | 553 | 0.2673 | 0.9245 |
| 0.384 | 18.99 | 584 | 0.2487 | 0.9265 |
| 0.3917 | 20.0 | 615 | 0.2353 | 0.9388 |
| 0.418 | 20.98 | 645 | 0.2113 | 0.9490 |
| 0.3662 | 21.98 | 676 | 0.2095 | 0.9327 |
| 0.3258 | 22.99 | 707 | 0.2139 | 0.9429 |
| 0.3268 | 24.0 | 738 | 0.1962 | 0.9449 |
| 0.3048 | 24.98 | 768 | 0.1935 | 0.9408 |
| 0.2696 | 25.98 | 799 | 0.2112 | 0.9408 |
| 0.2524 | 26.99 | 830 | 0.2310 | 0.9306 |
| 0.2491 | 28.0 | 861 | 0.1827 | 0.9449 |
| 0.2542 | 28.98 | 891 | 0.1720 | 0.9592 |
| 0.2898 | 29.98 | 922 | 0.1605 | 0.9490 |
| 0.2298 | 30.99 | 953 | 0.1326 | 0.9633 |
| 0.2137 | 32.0 | 984 | 0.1438 | 0.9571 |
| 0.2002 | 32.98 | 1014 | 0.1379 | 0.9551 |
| 0.2013 | 33.98 | 1045 | 0.1261 | 0.9653 |
| 0.1862 | 34.99 | 1076 | 0.1674 | 0.9408 |
| 0.1993 | 36.0 | 1107 | 0.1423 | 0.9571 |
| 0.2063 | 36.98 | 1137 | 0.1406 | 0.9592 |
| 0.2088 | 37.98 | 1168 | 0.1717 | 0.9429 |
| 0.1711 | 38.99 | 1199 | 0.1539 | 0.9510 |
| 0.1804 | 40.0 | 1230 | 0.1421 | 0.9571 |
| 0.1793 | 40.98 | 1260 | 0.0765 | 0.9776 |
| 0.2139 | 41.98 | 1291 | 0.1859 | 0.9449 |
| 0.1678 | 42.99 | 1322 | 0.1067 | 0.9796 |
| 0.1675 | 44.0 | 1353 | 0.0985 | 0.9735 |
| 0.1681 | 44.98 | 1383 | 0.1093 | 0.9653 |
| 0.1625 | 45.98 | 1414 | 0.1402 | 0.9592 |
| 0.1987 | 46.99 | 1445 | 0.1250 | 0.9673 |
| 0.1728 | 48.0 | 1476 | 0.1293 | 0.9633 |
| 0.1337 | 48.78 | 1500 | 0.1172 | 0.9633 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.14.1 |