---
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: 0.9102040816326531
---

<!-- 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: 0.3980
- Accuracy: 0.9102

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.9105        | 0.98  | 30   | 3.7931          | 0.0551   |
| 3.2821        | 1.98  | 61   | 2.9878          | 0.2755   |
| 2.4752        | 2.99  | 92   | 2.1760          | 0.4408   |
| 1.9958        | 4.0   | 123  | 1.6964          | 0.5327   |
| 1.6609        | 4.98  | 153  | 1.4001          | 0.6265   |
| 1.4328        | 5.98  | 184  | 1.1766          | 0.6796   |
| 1.2677        | 6.99  | 215  | 1.0262          | 0.7163   |
| 1.1174        | 8.0   | 246  | 0.8758          | 0.7653   |
| 1.0564        | 8.98  | 276  | 0.7675          | 0.8184   |
| 0.9003        | 9.98  | 307  | 0.7161          | 0.8286   |
| 0.8711        | 10.99 | 338  | 0.6461          | 0.8224   |
| 0.7954        | 12.0  | 369  | 0.5683          | 0.8653   |
| 0.743         | 12.98 | 399  | 0.5438          | 0.8510   |
| 0.6914        | 13.98 | 430  | 0.5129          | 0.8878   |
| 0.6714        | 14.99 | 461  | 0.4418          | 0.8857   |
| 0.663         | 16.0  | 492  | 0.4555          | 0.8694   |
| 0.6326        | 16.98 | 522  | 0.4746          | 0.8755   |
| 0.5831        | 17.98 | 553  | 0.4263          | 0.8776   |
| 0.571         | 18.99 | 584  | 0.4305          | 0.8857   |
| 0.6543        | 19.51 | 600  | 0.3980          | 0.9102   |


### Framework versions

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