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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-cells-separated-dataset-agregates-25
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: 0.9400918591493044
---
<!-- 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-cells-separated-dataset-agregates-25
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: 0.1790
- Accuracy: 0.9401
## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.351 | 0.9937 | 119 | 0.2523 | 0.9151 |
| 0.3364 | 1.9958 | 239 | 0.2355 | 0.9195 |
| 0.2999 | 2.9979 | 359 | 0.2384 | 0.9169 |
| 0.2861 | 4.0 | 479 | 0.1902 | 0.9341 |
| 0.3014 | 4.9937 | 598 | 0.2154 | 0.9290 |
| 0.292 | 5.9958 | 718 | 0.1764 | 0.9383 |
| 0.2441 | 6.9979 | 838 | 0.1894 | 0.9348 |
| 0.2416 | 8.0 | 958 | 0.1913 | 0.9349 |
| 0.2642 | 8.9937 | 1077 | 0.1738 | 0.9385 |
| 0.2482 | 9.9958 | 1197 | 0.1911 | 0.9371 |
| 0.2279 | 10.9979 | 1317 | 0.1867 | 0.9381 |
| 0.2331 | 12.0 | 1437 | 0.1814 | 0.9389 |
| 0.2208 | 12.9937 | 1556 | 0.1790 | 0.9401 |
| 0.2326 | 13.9958 | 1676 | 0.1926 | 0.9366 |
| 0.1899 | 14.9979 | 1796 | 0.1975 | 0.9372 |
| 0.1822 | 16.0 | 1916 | 0.2052 | 0.9352 |
| 0.1837 | 16.9937 | 2035 | 0.2078 | 0.9364 |
| 0.1712 | 17.9958 | 2155 | 0.2345 | 0.9288 |
| 0.1715 | 18.9979 | 2275 | 0.2156 | 0.9368 |
| 0.1516 | 20.0 | 2395 | 0.2279 | 0.9368 |
| 0.1504 | 20.9937 | 2514 | 0.2213 | 0.9382 |
| 0.139 | 21.9958 | 2634 | 0.2247 | 0.9370 |
| 0.1264 | 22.9979 | 2754 | 0.2357 | 0.9384 |
| 0.1266 | 24.0 | 2874 | 0.2360 | 0.9381 |
| 0.1144 | 24.8434 | 2975 | 0.2370 | 0.9375 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1
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