---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-50-LongSleeveCleanedData
  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: 0.9787709497206704
---

<!-- 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-50-LongSleeveCleanedData

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0889
- Accuracy: 0.9788

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 7
- total_train_batch_size: 56
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9906        | 0.99  | 143  | 1.0394          | 0.6134   |
| 0.7315        | 2.0   | 287  | 0.6790          | 0.7631   |
| 0.559         | 3.0   | 431  | 0.4735          | 0.8547   |
| 0.4905        | 4.0   | 575  | 0.3148          | 0.8983   |
| 0.3465        | 5.0   | 719  | 0.2225          | 0.9363   |
| 0.3372        | 6.0   | 863  | 0.1839          | 0.9486   |
| 0.3349        | 7.0   | 1007 | 0.1617          | 0.9587   |
| 0.3159        | 7.99  | 1150 | 0.1323          | 0.9620   |
| 0.2805        | 9.0   | 1294 | 0.1660          | 0.9587   |
| 0.2657        | 10.0  | 1438 | 0.1456          | 0.9531   |
| 0.2929        | 11.0  | 1582 | 0.1086          | 0.9698   |
| 0.2763        | 12.0  | 1726 | 0.0886          | 0.9765   |
| 0.2475        | 13.0  | 1870 | 0.1041          | 0.9732   |
| 0.2148        | 14.0  | 2014 | 0.0955          | 0.9777   |
| 0.209         | 14.99 | 2157 | 0.1061          | 0.9709   |
| 0.2408        | 16.0  | 2301 | 0.0784          | 0.9743   |
| 0.222         | 17.0  | 2445 | 0.0839          | 0.9698   |
| 0.208         | 18.0  | 2589 | 0.0873          | 0.9732   |
| 0.2214        | 19.0  | 2733 | 0.0889          | 0.9788   |
| 0.2375        | 19.88 | 2860 | 0.0864          | 0.9743   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3