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# resnet_50_base_aihub_model_py

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: 0.0987
- Accuracy: 0.9681
- Precision: 0.9712
- Recall: 0.9624
- F1: 0.9667

## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5577        | 1.0   | 149  | 0.4027          | 0.8453   | 0.8514    | 0.8415 | 0.8435 |
| 0.323         | 2.0   | 299  | 0.2346          | 0.9097   | 0.9208    | 0.8962 | 0.9074 |
| 0.2467        | 3.0   | 448  | 0.1786          | 0.9303   | 0.9465    | 0.9216 | 0.9326 |
| 0.1953        | 4.0   | 598  | 0.1266          | 0.9573   | 0.9591    | 0.9483 | 0.9535 |
| 0.1456        | 4.98  | 745  | 0.0987          | 0.9681   | 0.9712    | 0.9624 | 0.9667 |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3

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