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# resnet-50

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1947
- Accuracy: 0.5408

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5588        | 1.0   | 252  | 1.4406          | 0.4558   |
| 1.4831        | 2.0   | 505  | 1.3683          | 0.4790   |
| 1.4776        | 3.0   | 757  | 1.3199          | 0.4937   |
| 1.4246        | 4.0   | 1010 | 1.2881          | 0.5068   |
| 1.4102        | 5.0   | 1262 | 1.2469          | 0.5247   |
| 1.3806        | 6.0   | 1515 | 1.2276          | 0.5258   |
| 1.3861        | 7.0   | 1767 | 1.2121          | 0.5411   |
| 1.3791        | 8.0   | 2020 | 1.2075          | 0.5433   |
| 1.3683        | 9.0   | 2272 | 1.2011          | 0.5422   |
| 1.4119        | 9.98  | 2520 | 1.1947          | 0.5408   |


### Framework versions

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

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