---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-50-shortSleeveCleanedData
  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: 0.9781420765027322
---

<!-- 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-shortSleeveCleanedData

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: 0.1103
- Accuracy: 0.9781

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.973         | 1.0   | 147  | 0.9371          | 0.7268   |
| 0.6565        | 2.0   | 294  | 0.5520          | 0.8710   |
| 0.4609        | 3.0   | 441  | 0.2983          | 0.9279   |
| 0.3937        | 4.0   | 588  | 0.2051          | 0.9486   |
| 0.3723        | 5.0   | 735  | 0.1521          | 0.9727   |
| 0.3926        | 6.0   | 882  | 0.1490          | 0.9672   |
| 0.3326        | 7.0   | 1029 | 0.1367          | 0.9650   |
| 0.3166        | 8.0   | 1176 | 0.1109          | 0.9738   |
| 0.3492        | 9.0   | 1323 | 0.1108          | 0.9760   |
| 0.3228        | 10.0  | 1470 | 0.1103          | 0.9781   |


### Framework versions

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