---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-18
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6425188074672611
---

<!-- 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. -->

# resnet-18

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9403
- Accuracy: 0.6425

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4726        | 1.0   | 252  | 1.3072          | 0.5068   |
| 1.2683        | 2.0   | 505  | 1.0996          | 0.5865   |
| 1.2177        | 3.0   | 757  | 1.0444          | 0.6096   |
| 1.1636        | 4.0   | 1010 | 1.0185          | 0.6096   |
| 1.1372        | 5.0   | 1262 | 0.9945          | 0.6205   |
| 1.113         | 6.0   | 1515 | 0.9703          | 0.6342   |
| 1.0734        | 7.0   | 1767 | 0.9574          | 0.6333   |
| 1.0501        | 8.0   | 2020 | 0.9503          | 0.6375   |
| 1.0361        | 9.0   | 2272 | 0.9488          | 0.6389   |
| 1.0302        | 9.98  | 2520 | 0.9403          | 0.6425   |


### Framework versions

- Transformers 4.30.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3