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---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: minang_food_classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9277777777777778
---
<!-- 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. -->
# minang_food_classification
This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7860
- Accuracy: 0.9278
## 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-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3423 | 1.0 | 45 | 1.3263 | 0.7889 |
| 1.2638 | 2.0 | 90 | 1.2436 | 0.8278 |
| 1.2055 | 3.0 | 135 | 1.2503 | 0.8 |
| 1.14 | 4.0 | 180 | 1.1486 | 0.85 |
| 1.0908 | 5.0 | 225 | 1.0427 | 0.8778 |
| 1.0258 | 6.0 | 270 | 1.0210 | 0.8333 |
| 0.9776 | 7.0 | 315 | 0.9694 | 0.8722 |
| 0.9306 | 8.0 | 360 | 0.9379 | 0.8833 |
| 0.8985 | 9.0 | 405 | 0.9150 | 0.8778 |
| 0.8624 | 10.0 | 450 | 0.8884 | 0.8611 |
| 0.8243 | 11.0 | 495 | 0.8118 | 0.9222 |
| 0.8017 | 12.0 | 540 | 0.8394 | 0.8833 |
| 0.797 | 13.0 | 585 | 0.7761 | 0.9056 |
| 0.7765 | 14.0 | 630 | 0.7891 | 0.9111 |
| 0.7834 | 15.0 | 675 | 0.7945 | 0.8889 |
| 0.7483 | 16.0 | 720 | 0.7801 | 0.9 |
| 0.74 | 17.0 | 765 | 0.7524 | 0.9167 |
| 0.7315 | 18.0 | 810 | 0.7655 | 0.9111 |
| 0.7468 | 19.0 | 855 | 0.7860 | 0.8833 |
| 0.7393 | 20.0 | 900 | 0.7900 | 0.9056 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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