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# convnextv2-base-1k-224-for-pre_evaluation

This model is a fine-tuned version of [facebook/convnextv2-base-1k-224](https://huggingface.co/facebook/convnextv2-base-1k-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3599
- Accuracy: 0.4190

## 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6           | 1.0   | 16   | 1.5316          | 0.2961   |
| 1.5084        | 2.0   | 32   | 1.5061          | 0.2849   |
| 1.5134        | 3.0   | 48   | 1.4968          | 0.3240   |
| 1.4663        | 4.0   | 64   | 1.4607          | 0.3352   |
| 1.4046        | 5.0   | 80   | 1.4509          | 0.3268   |
| 1.4085        | 6.0   | 96   | 1.4423          | 0.3883   |
| 1.3443        | 7.0   | 112  | 1.4005          | 0.4022   |
| 1.3025        | 8.0   | 128  | 1.3599          | 0.4190   |
| 1.2627        | 9.0   | 144  | 1.3638          | 0.3911   |
| 1.2099        | 10.0  | 160  | 1.4058          | 0.3492   |
| 1.2086        | 11.0  | 176  | 1.4431          | 0.3408   |
| 1.1393        | 12.0  | 192  | 1.4143          | 0.3492   |
| 1.1039        | 13.0  | 208  | 1.4305          | 0.3883   |
| 1.0551        | 14.0  | 224  | 1.5203          | 0.3520   |
| 1.0368        | 15.0  | 240  | 1.5117          | 0.3324   |
| 0.9753        | 16.0  | 256  | 1.4545          | 0.3771   |
| 0.938         | 17.0  | 272  | 1.5396          | 0.3352   |
| 0.899         | 18.0  | 288  | 1.5770          | 0.3408   |
| 0.8629        | 19.0  | 304  | 1.7106          | 0.3128   |
| 0.8674        | 20.0  | 320  | 1.5864          | 0.3352   |
| 0.7789        | 21.0  | 336  | 1.6129          | 0.3408   |
| 0.7426        | 22.0  | 352  | 1.6353          | 0.3603   |
| 0.7677        | 23.0  | 368  | 1.6793          | 0.3464   |
| 0.7172        | 24.0  | 384  | 1.6759          | 0.3575   |
| 0.6809        | 25.0  | 400  | 1.7013          | 0.3659   |
| 0.6619        | 26.0  | 416  | 1.7108          | 0.3631   |
| 0.6656        | 27.0  | 432  | 1.7327          | 0.3715   |
| 0.6258        | 28.0  | 448  | 1.7378          | 0.3547   |
| 0.6173        | 29.0  | 464  | 1.7461          | 0.3603   |
| 0.6214        | 30.0  | 480  | 1.7475          | 0.3520   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3

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