|
--- |
|
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: 0.8802816901408451 |
|
--- |
|
|
|
<!-- 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: 0.5013 |
|
- Accuracy: 0.8803 |
|
|
|
## 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 | 0.5219 | 0.8873 | |
|
| No log | 2.8571 | 5 | 0.5431 | 0.8732 | |
|
| No log | 4.0 | 7 | 0.5444 | 0.8732 | |
|
| No log | 4.5714 | 8 | 0.5336 | 0.8803 | |
|
| 0.4252 | 5.7143 | 10 | 0.5235 | 0.8873 | |
|
| 0.4252 | 6.8571 | 12 | 0.5269 | 0.8803 | |
|
| 0.4252 | 8.0 | 14 | 0.5106 | 0.8873 | |
|
| 0.4252 | 8.5714 | 15 | 0.5048 | 0.8873 | |
|
| 0.4252 | 9.7143 | 17 | 0.5013 | 0.8803 | |
|
| 0.4252 | 10.8571 | 19 | 0.5105 | 0.8803 | |
|
| 0.4413 | 12.0 | 21 | 0.5256 | 0.8803 | |
|
| 0.4413 | 12.5714 | 22 | 0.5303 | 0.8732 | |
|
| 0.4413 | 13.7143 | 24 | 0.5218 | 0.8662 | |
|
| 0.4413 | 14.8571 | 26 | 0.5188 | 0.8592 | |
|
| 0.4413 | 16.0 | 28 | 0.5202 | 0.8592 | |
|
| 0.4413 | 16.5714 | 29 | 0.5252 | 0.8592 | |
|
| 0.437 | 17.7143 | 31 | 0.5385 | 0.8592 | |
|
| 0.437 | 18.8571 | 33 | 0.5456 | 0.8592 | |
|
| 0.437 | 20.0 | 35 | 0.5409 | 0.8732 | |
|
| 0.437 | 20.5714 | 36 | 0.5375 | 0.8662 | |
|
| 0.437 | 21.7143 | 38 | 0.5356 | 0.8662 | |
|
| 0.4343 | 22.8571 | 40 | 0.5328 | 0.8803 | |
|
| 0.4343 | 24.0 | 42 | 0.5318 | 0.8803 | |
|
| 0.4343 | 24.5714 | 43 | 0.5330 | 0.8803 | |
|
| 0.4343 | 25.7143 | 45 | 0.5334 | 0.8803 | |
|
| 0.4343 | 26.8571 | 47 | 0.5332 | 0.8732 | |
|
| 0.4343 | 28.0 | 49 | 0.5341 | 0.8732 | |
|
| 0.4271 | 28.5714 | 50 | 0.5343 | 0.8732 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.44.2 |
|
- Pytorch 2.4.1+cu121 |
|
- Datasets 3.2.0 |
|
- Tokenizers 0.19.1 |
|
|
