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---
library_name: transformers
base_model: Melo1512/vit-msn-small-beta-fia-manually-enhanced-HSV_test_2
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-msn-small-beta-fia-manually-enhanced-HSV_test_3
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: 0.8732394366197183
---
<!-- 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-beta-fia-manually-enhanced-HSV_test_3
This model is a fine-tuned version of [Melo1512/vit-msn-small-beta-fia-manually-enhanced-HSV_test_2](https://huggingface.co/Melo1512/vit-msn-small-beta-fia-manually-enhanced-HSV_test_2) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5558
- Accuracy: 0.8732
## 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: 1e-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: 0.15
- num_epochs: 50
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log | 0.5714 | 1 | 0.5123 | 0.8873 |
| No log | 1.7143 | 3 | 0.5495 | 0.8662 |
| No log | 2.8571 | 5 | 0.6080 | 0.8592 |
| No log | 4.0 | 7 | 0.5591 | 0.8732 |
| No log | 4.5714 | 8 | 0.5464 | 0.8732 |
| 0.4241 | 5.7143 | 10 | 0.5982 | 0.8451 |
| 0.4241 | 6.8571 | 12 | 0.6497 | 0.8169 |
| 0.4241 | 8.0 | 14 | 0.5928 | 0.8521 |
| 0.4241 | 8.5714 | 15 | 0.5711 | 0.8521 |
| 0.4241 | 9.7143 | 17 | 0.5468 | 0.8732 |
| 0.4241 | 10.8571 | 19 | 0.5483 | 0.8521 |
| 0.4152 | 12.0 | 21 | 0.5783 | 0.8451 |
| 0.4152 | 12.5714 | 22 | 0.5835 | 0.8451 |
| 0.4152 | 13.7143 | 24 | 0.5668 | 0.8451 |
| 0.4152 | 14.8571 | 26 | 0.5556 | 0.8451 |
| 0.4152 | 16.0 | 28 | 0.5564 | 0.8451 |
| 0.4152 | 16.5714 | 29 | 0.5591 | 0.8451 |
| 0.4367 | 17.7143 | 31 | 0.5619 | 0.8592 |
| 0.4367 | 18.8571 | 33 | 0.5809 | 0.8592 |
| 0.4367 | 20.0 | 35 | 0.5810 | 0.8662 |
| 0.4367 | 20.5714 | 36 | 0.5768 | 0.8662 |
| 0.4367 | 21.7143 | 38 | 0.5591 | 0.8732 |
| 0.4241 | 22.8571 | 40 | 0.5452 | 0.8732 |
| 0.4241 | 24.0 | 42 | 0.5387 | 0.8732 |
| 0.4241 | 24.5714 | 43 | 0.5398 | 0.8732 |
| 0.4241 | 25.7143 | 45 | 0.5458 | 0.8732 |
| 0.4241 | 26.8571 | 47 | 0.5509 | 0.8732 |
| 0.4241 | 28.0 | 49 | 0.5550 | 0.8732 |
| 0.4171 | 28.5714 | 50 | 0.5558 | 0.8732 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1
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