---
license: apache-2.0
base_model: microsoft/resnet-18
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: 1.0
---

<!-- 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 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: 0.7117
- Accuracy: 1.0

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log        | 0.9091 | 5    | 1.0318          | 0.4156   |
| 1.0893        | 2.0    | 11   | 0.9520          | 0.6364   |
| 1.0893        | 2.9091 | 16   | 0.9017          | 0.8442   |
| 0.9912        | 4.0    | 22   | 0.8444          | 1.0      |
| 0.9912        | 4.9091 | 27   | 0.8027          | 1.0      |
| 0.9248        | 6.0    | 33   | 0.7631          | 1.0      |
| 0.9248        | 6.9091 | 38   | 0.7369          | 1.0      |
| 0.8716        | 8.0    | 44   | 0.7156          | 1.0      |
| 0.8716        | 8.9091 | 49   | 0.7137          | 1.0      |
| 0.8517        | 9.0909 | 50   | 0.7117          | 1.0      |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1