---
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: 0.9040816326530612
---

<!-- 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:
- Loss: 0.3626
- Accuracy: 0.9041

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.929         | 0.98  | 30   | 3.8215          | 0.0429   |
| 3.2162        | 1.98  | 61   | 2.9144          | 0.2816   |
| 2.4387        | 2.99  | 92   | 2.1019          | 0.4776   |
| 1.9404        | 4.0   | 123  | 1.5607          | 0.6041   |
| 1.5756        | 4.98  | 153  | 1.3012          | 0.6449   |
| 1.3374        | 5.98  | 184  | 1.0699          | 0.7102   |
| 1.1912        | 6.99  | 215  | 0.9145          | 0.7633   |
| 1.0716        | 8.0   | 246  | 0.7864          | 0.7898   |
| 0.9751        | 8.98  | 276  | 0.6894          | 0.8204   |
| 0.8211        | 9.98  | 307  | 0.6256          | 0.8510   |
| 0.8254        | 10.99 | 338  | 0.5563          | 0.8633   |
| 0.742         | 12.0  | 369  | 0.5149          | 0.8694   |
| 0.6949        | 12.98 | 399  | 0.4625          | 0.8878   |
| 0.6401        | 13.98 | 430  | 0.4799          | 0.8857   |
| 0.6304        | 14.99 | 461  | 0.3970          | 0.8980   |
| 0.6239        | 16.0  | 492  | 0.4016          | 0.9      |
| 0.5911        | 16.98 | 522  | 0.4271          | 0.8755   |
| 0.5764        | 17.98 | 553  | 0.3922          | 0.9      |
| 0.5461        | 18.99 | 584  | 0.3750          | 0.9      |
| 0.6236        | 19.51 | 600  | 0.3626          | 0.9041   |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.14.1