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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-corect_dataset_lateral_flow_ivalidation
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: 0.8791208791208791
---
<!-- 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-corect_dataset_lateral_flow_ivalidation
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: 0.3307
- Accuracy: 0.8791
## 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: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log | 0.9231 | 3 | 0.6350 | 0.6337 |
| No log | 1.8462 | 6 | 0.5047 | 0.8022 |
| No log | 2.7692 | 9 | 0.3701 | 0.8791 |
| 0.5485 | 4.0 | 13 | 0.5379 | 0.7436 |
| 0.5485 | 4.9231 | 16 | 0.2748 | 0.8938 |
| 0.5485 | 5.8462 | 19 | 0.3004 | 0.8974 |
| 0.3335 | 6.7692 | 22 | 0.3492 | 0.8681 |
| 0.3335 | 8.0 | 26 | 0.2497 | 0.8974 |
| 0.3335 | 8.9231 | 29 | 0.4304 | 0.8315 |
| 0.3087 | 9.8462 | 32 | 0.3479 | 0.8791 |
| 0.3087 | 10.7692 | 35 | 0.3796 | 0.8645 |
| 0.3087 | 12.0 | 39 | 0.4152 | 0.8352 |
| 0.2614 | 12.9231 | 42 | 0.3199 | 0.9011 |
| 0.2614 | 13.8462 | 45 | 0.3434 | 0.8718 |
| 0.2614 | 14.7692 | 48 | 0.4001 | 0.8462 |
| 0.2471 | 16.0 | 52 | 0.3220 | 0.8901 |
| 0.2471 | 16.9231 | 55 | 0.3540 | 0.8718 |
| 0.2471 | 17.8462 | 58 | 0.4019 | 0.8535 |
| 0.2817 | 18.7692 | 61 | 0.3152 | 0.8974 |
| 0.2817 | 20.0 | 65 | 0.3978 | 0.8571 |
| 0.2817 | 20.9231 | 68 | 0.4289 | 0.8388 |
| 0.2353 | 21.8462 | 71 | 0.3146 | 0.8974 |
| 0.2353 | 22.7692 | 74 | 0.3206 | 0.8864 |
| 0.2353 | 24.0 | 78 | 0.3715 | 0.8828 |
| 0.2339 | 24.9231 | 81 | 0.3446 | 0.8938 |
| 0.2339 | 25.8462 | 84 | 0.2930 | 0.9048 |
| 0.2339 | 26.7692 | 87 | 0.4349 | 0.8205 |
| 0.2301 | 28.0 | 91 | 0.3630 | 0.8681 |
| 0.2301 | 28.9231 | 94 | 0.3669 | 0.8645 |
| 0.2301 | 29.8462 | 97 | 0.5037 | 0.7912 |
| 0.2115 | 30.7692 | 100 | 0.3449 | 0.8828 |
| 0.2115 | 32.0 | 104 | 0.3280 | 0.9011 |
| 0.2115 | 32.9231 | 107 | 0.4031 | 0.8425 |
| 0.2033 | 33.8462 | 110 | 0.3612 | 0.8535 |
| 0.2033 | 34.7692 | 113 | 0.3163 | 0.8901 |
| 0.2033 | 36.0 | 117 | 0.3234 | 0.8864 |
| 0.1807 | 36.9231 | 120 | 0.3307 | 0.8791 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1
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