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# ViT_bloodmnist_std_60

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3387
- Accuracy: 0.8913
- F1: 0.8681

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
| 0.7924        | 0.0595 | 200   | 1.7254          | 0.4106   | 0.3373 |
| 0.4527        | 0.1189 | 400   | 1.2129          | 0.6641   | 0.5830 |
| 0.4004        | 0.1784 | 600   | 0.9461          | 0.7547   | 0.6592 |
| 0.3657        | 0.2378 | 800   | 0.7647          | 0.8084   | 0.7425 |
| 0.3506        | 0.2973 | 1000  | 0.6377          | 0.8043   | 0.7888 |
| 0.3081        | 0.3567 | 1200  | 0.6453          | 0.8055   | 0.7728 |
| 0.2848        | 0.4162 | 1400  | 0.6006          | 0.8195   | 0.7385 |
| 0.28          | 0.4756 | 1600  | 0.8017          | 0.7097   | 0.6680 |
| 0.3041        | 0.5351 | 1800  | 0.4496          | 0.8586   | 0.8187 |
| 0.272         | 0.5945 | 2000  | 0.7200          | 0.7541   | 0.7126 |
| 0.259         | 0.6540 | 2200  | 0.5110          | 0.8131   | 0.7867 |
| 0.2524        | 0.7134 | 2400  | 0.4057          | 0.8633   | 0.8343 |
| 0.2439        | 0.7729 | 2600  | 0.4060          | 0.8604   | 0.8288 |
| 0.2422        | 0.8323 | 2800  | 0.4496          | 0.8627   | 0.8229 |
| 0.2332        | 0.8918 | 3000  | 0.4147          | 0.8586   | 0.8157 |
| 0.2192        | 0.9512 | 3200  | 0.3414          | 0.8756   | 0.8578 |
| 0.212         | 1.0107 | 3400  | 0.4139          | 0.8464   | 0.8048 |
| 0.1738        | 1.0702 | 3600  | 0.5111          | 0.8213   | 0.7703 |
| 0.1718        | 1.1296 | 3800  | 0.3725          | 0.8674   | 0.8398 |
| 0.1679        | 1.1891 | 4000  | 0.4632          | 0.8400   | 0.8283 |
| 0.1706        | 1.2485 | 4200  | 0.4331          | 0.8511   | 0.8216 |
| 0.1602        | 1.3080 | 4400  | 0.4359          | 0.8382   | 0.8094 |
| 0.1502        | 1.3674 | 4600  | 0.5608          | 0.7903   | 0.7278 |
| 0.1713        | 1.4269 | 4800  | 0.3495          | 0.8762   | 0.8555 |
| 0.1544        | 1.4863 | 5000  | 0.5389          | 0.8072   | 0.7830 |
| 0.1477        | 1.5458 | 5200  | 0.3790          | 0.8645   | 0.8318 |
| 0.1515        | 1.6052 | 5400  | 0.4332          | 0.8300   | 0.7977 |
| 0.1465        | 1.6647 | 5600  | 0.5368          | 0.8230   | 0.7546 |
| 0.1409        | 1.7241 | 5800  | 0.4630          | 0.8493   | 0.8004 |
| 0.1294        | 1.7836 | 6000  | 0.3530          | 0.8803   | 0.8396 |
| 0.1252        | 1.8430 | 6200  | 0.3822          | 0.875    | 0.8410 |
| 0.1273        | 1.9025 | 6400  | 0.2833          | 0.9042   | 0.8802 |
| 0.1196        | 1.9620 | 6600  | 0.3610          | 0.8791   | 0.8407 |
| 0.1018        | 2.0214 | 6800  | 0.3968          | 0.8581   | 0.8354 |
| 0.0692        | 2.0809 | 7000  | 0.4695          | 0.8458   | 0.8122 |
| 0.0674        | 2.1403 | 7200  | 0.4450          | 0.8534   | 0.8136 |
| 0.0615        | 2.1998 | 7400  | 0.3819          | 0.8721   | 0.8483 |
| 0.0574        | 2.2592 | 7600  | 0.3725          | 0.875    | 0.8468 |
| 0.067         | 2.3187 | 7800  | 0.4728          | 0.8481   | 0.8078 |
| 0.0684        | 2.3781 | 8000  | 0.3483          | 0.8873   | 0.8590 |
| 0.066         | 2.4376 | 8200  | 0.3763          | 0.8797   | 0.8514 |
| 0.0521        | 2.4970 | 8400  | 0.4029          | 0.8657   | 0.8377 |
| 0.0553        | 2.5565 | 8600  | 0.4100          | 0.8697   | 0.8382 |
| 0.0534        | 2.6159 | 8800  | 0.3810          | 0.8762   | 0.8469 |
| 0.0475        | 2.6754 | 9000  | 0.4043          | 0.8703   | 0.8416 |
| 0.054         | 2.7348 | 9200  | 0.4014          | 0.8762   | 0.8460 |
| 0.0526        | 2.7943 | 9400  | 0.4015          | 0.875    | 0.8439 |
| 0.0481        | 2.8537 | 9600  | 0.4047          | 0.8779   | 0.8455 |
| 0.0442        | 2.9132 | 9800  | 0.3997          | 0.8773   | 0.8449 |
| 0.0372        | 2.9727 | 10000 | 0.4131          | 0.8762   | 0.8433 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0

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