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# vit-base-HAM-10000-sharpened-patch-32

This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-patch32-224-in21k) on the ahishamm/HAM_db_sharpened dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4806
- Accuracy: 0.8369
- Recall: 0.8369
- F1: 0.8369
- Precision: 0.8369

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1     | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.8099        | 0.2   | 100  | 0.8060          | 0.7247   | 0.7247 | 0.7247 | 0.7247    |
| 0.7437        | 0.4   | 200  | 0.7020          | 0.7541   | 0.7541 | 0.7541 | 0.7541    |
| 0.7982        | 0.6   | 300  | 0.7352          | 0.7411   | 0.7411 | 0.7411 | 0.7411    |
| 0.7646        | 0.8   | 400  | 0.6603          | 0.7626   | 0.7626 | 0.7626 | 0.7626    |
| 0.6141        | 1.0   | 500  | 0.6373          | 0.7771   | 0.7771 | 0.7771 | 0.7771    |
| 0.5934        | 1.2   | 600  | 0.6141          | 0.7820   | 0.7820 | 0.7820 | 0.7820    |
| 0.5524        | 1.4   | 700  | 0.5621          | 0.8030   | 0.8030 | 0.8030 | 0.8030    |
| 0.5057        | 1.6   | 800  | 0.6074          | 0.7855   | 0.7855 | 0.7855 | 0.7855    |
| 0.5519        | 1.8   | 900  | 0.5486          | 0.7990   | 0.7990 | 0.7990 | 0.7990    |
| 0.4784        | 2.0   | 1000 | 0.5382          | 0.8060   | 0.8060 | 0.8060 | 0.8060    |
| 0.2592        | 2.2   | 1100 | 0.5237          | 0.8165   | 0.8165 | 0.8165 | 0.8165    |
| 0.3872        | 2.4   | 1200 | 0.5345          | 0.8120   | 0.8120 | 0.8120 | 0.8120    |
| 0.2506        | 2.59  | 1300 | 0.5061          | 0.8214   | 0.8214 | 0.8214 | 0.8214    |
| 0.2907        | 2.79  | 1400 | 0.4940          | 0.8354   | 0.8354 | 0.8354 | 0.8354    |
| 0.2436        | 2.99  | 1500 | 0.4806          | 0.8369   | 0.8369 | 0.8369 | 0.8369    |
| 0.1472        | 3.19  | 1600 | 0.5231          | 0.8219   | 0.8219 | 0.8219 | 0.8219    |
| 0.1441        | 3.39  | 1700 | 0.5452          | 0.8329   | 0.8329 | 0.8329 | 0.8329    |
| 0.1327        | 3.59  | 1800 | 0.5410          | 0.8354   | 0.8354 | 0.8354 | 0.8354    |
| 0.0615        | 3.79  | 1900 | 0.5473          | 0.8424   | 0.8424 | 0.8424 | 0.8424    |
| 0.0943        | 3.99  | 2000 | 0.5490          | 0.8409   | 0.8409 | 0.8409 | 0.8409    |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3

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