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# vit-base-beans

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3643
- Accuracy: 0.9027

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 3.5924        | 0.0620 | 100  | 3.5675          | 0.1927   |
| 3.0189        | 0.1239 | 200  | 3.0313          | 0.3047   |
| 2.5541        | 0.1859 | 300  | 2.5575          | 0.3956   |
| 2.114         | 0.2478 | 400  | 2.2332          | 0.4571   |
| 1.9624        | 0.3098 | 500  | 1.9455          | 0.5596   |
| 1.6749        | 0.3717 | 600  | 1.7370          | 0.5787   |
| 1.5852        | 0.4337 | 700  | 1.4947          | 0.6439   |
| 1.1875        | 0.4957 | 800  | 1.4151          | 0.6468   |
| 1.5114        | 0.5576 | 900  | 1.2709          | 0.6820   |
| 1.3122        | 0.6196 | 1000 | 1.1940          | 0.6939   |
| 1.0721        | 0.6815 | 1100 | 1.0757          | 0.7261   |
| 0.8249        | 0.7435 | 1200 | 0.9666          | 0.7576   |
| 0.7944        | 0.8055 | 1300 | 0.9101          | 0.7708   |
| 0.8032        | 0.8674 | 1400 | 0.9011          | 0.7691   |
| 0.7479        | 0.9294 | 1500 | 0.7409          | 0.8067   |
| 0.5997        | 0.9913 | 1600 | 0.7326          | 0.8110   |
| 0.5005        | 1.0533 | 1700 | 0.6769          | 0.8211   |
| 0.4107        | 1.1152 | 1800 | 0.6375          | 0.8374   |
| 0.4596        | 1.1772 | 1900 | 0.6302          | 0.8304   |
| 0.2544        | 1.2392 | 2000 | 0.5805          | 0.8400   |
| 0.2983        | 1.3011 | 2100 | 0.5480          | 0.8501   |
| 0.3214        | 1.3631 | 2200 | 0.5053          | 0.8683   |
| 0.2384        | 1.4250 | 2300 | 0.4929          | 0.8713   |
| 0.2397        | 1.4870 | 2400 | 0.4664          | 0.8742   |
| 0.3448        | 1.5489 | 2500 | 0.4690          | 0.8755   |
| 0.3129        | 1.6109 | 2600 | 0.4351          | 0.8843   |
| 0.1027        | 1.6729 | 2700 | 0.4311          | 0.8846   |
| 0.2086        | 1.7348 | 2800 | 0.4088          | 0.8897   |
| 0.1683        | 1.7968 | 2900 | 0.4133          | 0.8919   |
| 0.2767        | 1.8587 | 3000 | 0.3851          | 0.8964   |
| 0.1582        | 1.9207 | 3100 | 0.3703          | 0.9018   |
| 0.1421        | 1.9827 | 3200 | 0.3643          | 0.9027   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1

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