
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# ViT_bloodmnist_std_45

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: 0.2659
- Accuracy: 0.9065
- F1: 0.8909

## 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     |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
| 0.6113        | 0.0595 | 200   | 0.8908          | 0.6846   | 0.5917 |
| 0.3578        | 0.1189 | 400   | 0.5958          | 0.7956   | 0.7548 |
| 0.3118        | 0.1784 | 600   | 0.5688          | 0.7810   | 0.7132 |
| 0.2815        | 0.2378 | 800   | 0.5227          | 0.7961   | 0.7645 |
| 0.266         | 0.2973 | 1000  | 0.6554          | 0.7687   | 0.7229 |
| 0.2353        | 0.3567 | 1200  | 0.3328          | 0.8838   | 0.8615 |
| 0.2297        | 0.4162 | 1400  | 0.4696          | 0.8592   | 0.7990 |
| 0.2267        | 0.4756 | 1600  | 0.4362          | 0.8493   | 0.8117 |
| 0.2266        | 0.5351 | 1800  | 0.3286          | 0.8838   | 0.8407 |
| 0.2047        | 0.5945 | 2000  | 0.3614          | 0.8697   | 0.8382 |
| 0.1948        | 0.6540 | 2200  | 0.3144          | 0.8843   | 0.8546 |
| 0.1953        | 0.7134 | 2400  | 0.3805          | 0.8657   | 0.8180 |
| 0.1728        | 0.7729 | 2600  | 0.3364          | 0.8820   | 0.8339 |
| 0.1658        | 0.8323 | 2800  | 0.2873          | 0.8978   | 0.8743 |
| 0.1594        | 0.8918 | 3000  | 0.3062          | 0.8914   | 0.8580 |
| 0.1649        | 0.9512 | 3200  | 0.3313          | 0.8867   | 0.8577 |
| 0.1508        | 1.0107 | 3400  | 0.2117          | 0.9217   | 0.9133 |
| 0.1062        | 1.0702 | 3600  | 0.2978          | 0.8919   | 0.8756 |
| 0.1091        | 1.1296 | 3800  | 0.2832          | 0.9019   | 0.8831 |
| 0.0993        | 1.1891 | 4000  | 0.3275          | 0.8943   | 0.8718 |
| 0.1001        | 1.2485 | 4200  | 0.3420          | 0.8896   | 0.8568 |
| 0.1092        | 1.3080 | 4400  | 0.2594          | 0.9130   | 0.8909 |
| 0.092         | 1.3674 | 4600  | 0.3181          | 0.8966   | 0.8753 |
| 0.1036        | 1.4269 | 4800  | 0.2721          | 0.9048   | 0.8852 |
| 0.0896        | 1.4863 | 5000  | 0.3795          | 0.8820   | 0.8617 |
| 0.0904        | 1.5458 | 5200  | 0.2382          | 0.9171   | 0.8980 |
| 0.0864        | 1.6052 | 5400  | 0.3845          | 0.8814   | 0.8499 |
| 0.0809        | 1.6647 | 5600  | 0.3189          | 0.8984   | 0.8758 |
| 0.0764        | 1.7241 | 5800  | 0.3952          | 0.8843   | 0.8522 |
| 0.0796        | 1.7836 | 6000  | 0.3656          | 0.8867   | 0.8460 |
| 0.0695        | 1.8430 | 6200  | 0.3266          | 0.8925   | 0.8597 |
| 0.0682        | 1.9025 | 6400  | 0.3247          | 0.8960   | 0.8647 |
| 0.06          | 1.9620 | 6600  | 0.2349          | 0.9223   | 0.9055 |
| 0.0498        | 2.0214 | 6800  | 0.2578          | 0.9176   | 0.8952 |
| 0.0296        | 2.0809 | 7000  | 0.2592          | 0.9211   | 0.9070 |
| 0.0251        | 2.1403 | 7200  | 0.3249          | 0.9048   | 0.8797 |
| 0.02          | 2.1998 | 7400  | 0.2977          | 0.9165   | 0.8973 |
| 0.0274        | 2.2592 | 7600  | 0.3411          | 0.9013   | 0.8730 |
| 0.0241        | 2.3187 | 7800  | 0.3916          | 0.9013   | 0.8752 |
| 0.0253        | 2.3781 | 8000  | 0.2919          | 0.9136   | 0.8939 |
| 0.0197        | 2.4376 | 8200  | 0.3294          | 0.9077   | 0.8835 |
| 0.0209        | 2.4970 | 8400  | 0.3709          | 0.8966   | 0.8652 |
| 0.0175        | 2.5565 | 8600  | 0.3639          | 0.9001   | 0.8733 |
| 0.0191        | 2.6159 | 8800  | 0.3706          | 0.9048   | 0.8790 |
| 0.0167        | 2.6754 | 9000  | 0.3120          | 0.9171   | 0.8993 |
| 0.0224        | 2.7348 | 9200  | 0.3493          | 0.9048   | 0.8799 |
| 0.015         | 2.7943 | 9400  | 0.3398          | 0.9130   | 0.8889 |
| 0.0155        | 2.8537 | 9600  | 0.3707          | 0.9036   | 0.8758 |
| 0.0129        | 2.9132 | 9800  | 0.3467          | 0.9118   | 0.8909 |
| 0.0126        | 2.9727 | 10000 | 0.3470          | 0.9095   | 0.8874 |


### Framework versions

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

basophil eosinophil erythroblast immature granulocytes(myelocytes, metamyelocytes and promyelocytes) lymphocyte monocyte neutrophil platelet