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---
library_name: transformers
license: apache-2.0
base_model: facebook/vit-msn-small
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-msn-small-wbc-classifier-0316-cropped-cleaned-dataset-10
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8854679802955665
---
<!-- 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-msn-small-wbc-classifier-0316-cropped-cleaned-dataset-10
This model is a fine-tuned version of [facebook/vit-msn-small](https://huggingface.co/facebook/vit-msn-small) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3986
- Accuracy: 0.8855
## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3709 | 1.0 | 17 | 0.6977 | 0.8050 |
| 0.5673 | 2.0 | 34 | 0.5949 | 0.8099 |
| 0.5227 | 3.0 | 51 | 0.6152 | 0.7931 |
| 0.4958 | 4.0 | 68 | 0.4351 | 0.8436 |
| 0.4402 | 5.0 | 85 | 0.3777 | 0.8580 |
| 0.3878 | 6.0 | 102 | 0.3970 | 0.8699 |
| 0.3646 | 7.0 | 119 | 0.3793 | 0.8641 |
| 0.3452 | 8.0 | 136 | 0.3550 | 0.8805 |
| 0.344 | 9.0 | 153 | 0.4003 | 0.8736 |
| 0.3365 | 10.0 | 170 | 0.3654 | 0.8830 |
| 0.3223 | 11.0 | 187 | 0.3571 | 0.8764 |
| 0.2819 | 12.0 | 204 | 0.3665 | 0.8789 |
| 0.2998 | 13.0 | 221 | 0.3609 | 0.8838 |
| 0.2959 | 14.0 | 238 | 0.4335 | 0.8719 |
| 0.2662 | 15.0 | 255 | 0.4245 | 0.8785 |
| 0.2668 | 16.0 | 272 | 0.3760 | 0.8846 |
| 0.2576 | 17.0 | 289 | 0.3728 | 0.8830 |
| 0.2398 | 18.0 | 306 | 0.4192 | 0.8814 |
| 0.2278 | 19.0 | 323 | 0.4156 | 0.8805 |
| 0.2033 | 20.0 | 340 | 0.4159 | 0.8851 |
| 0.2037 | 21.0 | 357 | 0.3986 | 0.8855 |
| 0.1934 | 22.0 | 374 | 0.4220 | 0.8822 |
| 0.1983 | 23.0 | 391 | 0.4159 | 0.8855 |
| 0.1746 | 24.0 | 408 | 0.4179 | 0.8855 |
| 0.1776 | 25.0 | 425 | 0.4247 | 0.8834 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1
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