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---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- medmnist-v2
metrics:
- accuracy
- f1
model-index:
- name: ViT_breastmnist_std_0
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: medmnist-v2
type: medmnist-v2
config: breastmnist
split: validation
args: breastmnist
metrics:
- name: Accuracy
type: accuracy
value: 0.8717948717948718
- name: F1
type: f1
value: 0.8370927318295739
---
<!-- 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_breastmnist_std_0
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: 0.3272
- Accuracy: 0.8718
- F1: 0.8371
## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 0.3533 | 0.2597 | 20 | 0.3035 | 0.8846 | 0.8406 |
| 0.1354 | 0.5195 | 40 | 0.2280 | 0.8974 | 0.8655 |
| 0.0247 | 0.7792 | 60 | 0.2669 | 0.9231 | 0.8956 |
| 0.0089 | 1.0390 | 80 | 0.2742 | 0.9231 | 0.8956 |
| 0.003 | 1.2987 | 100 | 0.3404 | 0.9103 | 0.8803 |
| 0.0018 | 1.5584 | 120 | 0.3583 | 0.9231 | 0.8956 |
| 0.0013 | 1.8182 | 140 | 0.3720 | 0.9231 | 0.8956 |
| 0.0009 | 2.0779 | 160 | 0.3892 | 0.9231 | 0.8956 |
| 0.0007 | 2.3377 | 180 | 0.3933 | 0.9231 | 0.8956 |
| 0.0006 | 2.5974 | 200 | 0.3948 | 0.9231 | 0.8956 |
| 0.0005 | 2.8571 | 220 | 0.4038 | 0.9231 | 0.8956 |
| 0.0005 | 3.1169 | 240 | 0.4145 | 0.9231 | 0.8956 |
| 0.0004 | 3.3766 | 260 | 0.4176 | 0.9231 | 0.8956 |
| 0.0004 | 3.6364 | 280 | 0.4230 | 0.9231 | 0.8956 |
| 0.0003 | 3.8961 | 300 | 0.4274 | 0.9103 | 0.8803 |
| 0.0003 | 4.1558 | 320 | 0.4344 | 0.9231 | 0.8956 |
| 0.0003 | 4.4156 | 340 | 0.4380 | 0.9231 | 0.8956 |
| 0.0003 | 4.6753 | 360 | 0.4406 | 0.9103 | 0.8803 |
| 0.0003 | 4.9351 | 380 | 0.4459 | 0.9231 | 0.8956 |
| 0.0002 | 5.1948 | 400 | 0.4472 | 0.9103 | 0.8803 |
| 0.0002 | 5.4545 | 420 | 0.4514 | 0.9103 | 0.8803 |
| 0.0002 | 5.7143 | 440 | 0.4550 | 0.9231 | 0.8956 |
| 0.0002 | 5.9740 | 460 | 0.4579 | 0.9231 | 0.8956 |
| 0.0002 | 6.2338 | 480 | 0.4600 | 0.9231 | 0.8956 |
| 0.0002 | 6.4935 | 500 | 0.4614 | 0.9103 | 0.8803 |
| 0.0002 | 6.7532 | 520 | 0.4637 | 0.9231 | 0.8956 |
| 0.0002 | 7.0130 | 540 | 0.4660 | 0.9231 | 0.8956 |
| 0.0002 | 7.2727 | 560 | 0.4684 | 0.9231 | 0.8956 |
| 0.0002 | 7.5325 | 580 | 0.4695 | 0.9231 | 0.8956 |
| 0.0002 | 7.7922 | 600 | 0.4710 | 0.9103 | 0.8803 |
| 0.0001 | 8.0519 | 620 | 0.4719 | 0.9103 | 0.8803 |
| 0.0001 | 8.3117 | 640 | 0.4726 | 0.9103 | 0.8803 |
| 0.0001 | 8.5714 | 660 | 0.4742 | 0.9103 | 0.8803 |
| 0.0001 | 8.8312 | 680 | 0.4754 | 0.9231 | 0.8956 |
| 0.0002 | 9.0909 | 700 | 0.4757 | 0.9231 | 0.8956 |
| 0.0001 | 9.3506 | 720 | 0.4759 | 0.9231 | 0.8956 |
| 0.0001 | 9.6104 | 740 | 0.4763 | 0.9231 | 0.8956 |
| 0.0001 | 9.8701 | 760 | 0.4765 | 0.9231 | 0.8956 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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