|
--- |
|
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: |
|
- src: hf_samples/ArchitectsDaughter-Regular_1.png |
|
example_title: Architects Daughter |
|
- src: main/hf_samples/Courier_28.png |
|
example_title: Courier |
|
- src: main/hf_samples/Helvetica_3.png |
|
example_title: Helvetica |
|
- src: hf_samples/IBMPlexSans-Regular_25.png |
|
example_title: IBM Plex Sans |
|
- src: hf_samples/Inter-Regular_43.png |
|
example_title: Inter |
|
- src: hf_samples/Lobster-Regular_25.png |
|
example_title: Lobster |
|
- src: hf_samples/Trebuchet_MS_11.png |
|
example_title: Trebuchet MS |
|
- src: 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. |
|
Result: Loss: 0.1172; Accuracy: 0.9633 |
|
|
|
Try with any screenshot of a font, or any of the examples in [the 'samples' subfolder of this repo](https://huggingface.co/gaborcselle/font-identifier/tree/main/hf_samples). |
|
|
|
## 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), and [on Threads.net](https://www.threads.net/@gaborcselle/post/CzZJpJCpxTz). |
|
|
|
The code used to build this model is in this github rep |
|
|
|
## 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 | |
|
| 0.8309 | 10.99 | 338 | 0.5536 | 0.8551 | |
|
| 0.3917 | 20.0 | 615 | 0.2353 | 0.9388 | |
|
| 0.2298 | 30.99 | 953 | 0.1326 | 0.9633 | |
|
| 0.1804 | 40.0 | 1230 | 0.1421 | 0.9571 | |
|
| 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 | |
|
|
|
### Confusion Matrix |
|
|
|
Confusion matrix on test data. |
|
|
|
 |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.36.0.dev0 |
|
- Pytorch 2.0.0 |
|
- Datasets 2.12.0 |
|
- Tokenizers 0.14.1 |
