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# plant-vit-model-4-final

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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0741
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0

## 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.2499        | 1.0   | 83   | 0.9951          | 0.9157    | 0.9157 | 0.9157 | 0.9157   |
| 0.4403        | 2.0   | 166  | 0.3535          | 0.9845    | 0.9845 | 0.9845 | 0.9845   |
| 0.2681        | 3.0   | 249  | 0.2108          | 0.9973    | 0.9973 | 0.9973 | 0.9973   |
| 0.2026        | 4.0   | 332  | 0.1501          | 0.9984    | 0.9984 | 0.9984 | 0.9984   |
| 0.158         | 5.0   | 415  | 0.1212          | 0.9984    | 0.9984 | 0.9984 | 0.9984   |
| 0.1382        | 6.0   | 498  | 0.1024          | 0.9984    | 0.9984 | 0.9984 | 0.9984   |
| 0.1233        | 7.0   | 581  | 0.0882          | 0.9995    | 0.9995 | 0.9995 | 0.9995   |
| 0.1026        | 8.0   | 664  | 0.0790          | 0.9995    | 0.9995 | 0.9995 | 0.9995   |
| 0.1046        | 9.0   | 747  | 0.0741          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0881        | 10.0  | 830  | 0.0731          | 1.0       | 1.0    | 1.0    | 1.0      |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3

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