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--- |
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license: mit |
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base_model: microsoft/resnet-18 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- gaborcselle/font-examples |
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metrics: |
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- accuracy |
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model-index: |
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- name: font-identifier |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: test |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.963265306122449 |
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widget: |
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- src: hf_samples/ArchitectsDaughter-Regular_1.png |
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example_title: Architects Daughter |
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- src: main/hf_samples/Courier_28.png |
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example_title: Courier |
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- src: main/hf_samples/Helvetica_3.png |
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example_title: Helvetica |
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- src: hf_samples/IBMPlexSans-Regular_25.png |
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example_title: IBM Plex Sans |
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- src: hf_samples/Inter-Regular_43.png |
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example_title: Inter |
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- src: hf_samples/Lobster-Regular_25.png |
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example_title: Lobster |
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- src: hf_samples/Trebuchet_MS_11.png |
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example_title: Trebuchet MS |
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- src: hf_samples/Verdana_Bold_43.png |
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example_title: Verdana Bold |
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language: |
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- en |
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--- |
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# font-identifier |
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This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset. |
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Result: Loss: 0.1172; Accuracy: 0.9633 |
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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). |
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## Model description |
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Identify the font used in an image. Visual classifier based on ResNet18. |
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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). |
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## Intended uses & limitations |
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Identify any of 48 standard fonts from the training data. |
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## Training and evaluation data |
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Trained and eval'd on the [gaborcselle/font-examples](https://huggingface.co/datasets/gaborcselle/font-examples) dataset (80/20 split). |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 4.0243 | 0.98 | 30 | 3.9884 | 0.0204 | |
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| 0.8309 | 10.99 | 338 | 0.5536 | 0.8551 | |
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| 0.3917 | 20.0 | 615 | 0.2353 | 0.9388 | |
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| 0.2298 | 30.99 | 953 | 0.1326 | 0.9633 | |
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| 0.1804 | 40.0 | 1230 | 0.1421 | 0.9571 | |
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| 0.1987 | 46.99 | 1445 | 0.1250 | 0.9673 | |
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| 0.1728 | 48.0 | 1476 | 0.1293 | 0.9633 | |
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| 0.1337 | 48.78 | 1500 | 0.1172 | 0.9633 | |
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### Framework versions |
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- Transformers 4.36.0.dev0 |
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- Pytorch 2.0.0 |
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- Datasets 2.12.0 |
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- Tokenizers 0.14.1 |
