metadata
license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_10x_beit_large_adamax_001_fold4
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: 0.8716666666666667
smids_10x_beit_large_adamax_001_fold4
This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.6842
- Accuracy: 0.8717
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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3361 | 1.0 | 750 | 0.4333 | 0.8367 |
| 0.2968 | 2.0 | 1500 | 0.4495 | 0.8467 |
| 0.288 | 3.0 | 2250 | 0.4264 | 0.8383 |
| 0.2379 | 4.0 | 3000 | 0.4907 | 0.85 |
| 0.1893 | 5.0 | 3750 | 0.4876 | 0.8533 |
| 0.1419 | 6.0 | 4500 | 0.4376 | 0.8667 |
| 0.1288 | 7.0 | 5250 | 0.5742 | 0.84 |
| 0.079 | 8.0 | 6000 | 0.6426 | 0.86 |
| 0.0885 | 9.0 | 6750 | 0.6694 | 0.8617 |
| 0.0513 | 10.0 | 7500 | 0.7772 | 0.8483 |
| 0.0371 | 11.0 | 8250 | 0.7425 | 0.8667 |
| 0.0559 | 12.0 | 9000 | 0.7844 | 0.8633 |
| 0.0437 | 13.0 | 9750 | 0.9475 | 0.8617 |
| 0.0237 | 14.0 | 10500 | 0.8539 | 0.86 |
| 0.0064 | 15.0 | 11250 | 1.1662 | 0.8683 |
| 0.0766 | 16.0 | 12000 | 1.1003 | 0.8683 |
| 0.0045 | 17.0 | 12750 | 1.1294 | 0.8633 |
| 0.0012 | 18.0 | 13500 | 1.0595 | 0.8717 |
| 0.0107 | 19.0 | 14250 | 1.0246 | 0.875 |
| 0.0098 | 20.0 | 15000 | 0.9670 | 0.8633 |
| 0.0227 | 21.0 | 15750 | 1.0829 | 0.8633 |
| 0.0004 | 22.0 | 16500 | 1.0091 | 0.855 |
| 0.0026 | 23.0 | 17250 | 1.0123 | 0.8667 |
| 0.001 | 24.0 | 18000 | 1.0183 | 0.8783 |
| 0.0083 | 25.0 | 18750 | 1.2133 | 0.8533 |
| 0.0076 | 26.0 | 19500 | 1.0638 | 0.865 |
| 0.0045 | 27.0 | 20250 | 1.1546 | 0.8717 |
| 0.0001 | 28.0 | 21000 | 1.0902 | 0.8567 |
| 0.0003 | 29.0 | 21750 | 1.1809 | 0.86 |
| 0.0 | 30.0 | 22500 | 1.2715 | 0.8733 |
| 0.0001 | 31.0 | 23250 | 1.1922 | 0.8767 |
| 0.0 | 32.0 | 24000 | 1.4076 | 0.87 |
| 0.0075 | 33.0 | 24750 | 1.3961 | 0.8617 |
| 0.0 | 34.0 | 25500 | 1.4345 | 0.875 |
| 0.0 | 35.0 | 26250 | 1.6125 | 0.8683 |
| 0.0 | 36.0 | 27000 | 1.5456 | 0.8567 |
| 0.0 | 37.0 | 27750 | 1.5632 | 0.865 |
| 0.0 | 38.0 | 28500 | 1.6349 | 0.8617 |
| 0.0 | 39.0 | 29250 | 1.5362 | 0.8617 |
| 0.0 | 40.0 | 30000 | 1.6434 | 0.8667 |
| 0.0 | 41.0 | 30750 | 1.6815 | 0.87 |
| 0.0 | 42.0 | 31500 | 1.6593 | 0.8667 |
| 0.0 | 43.0 | 32250 | 1.6757 | 0.87 |
| 0.0 | 44.0 | 33000 | 1.6503 | 0.8683 |
| 0.0 | 45.0 | 33750 | 1.6999 | 0.8667 |
| 0.0 | 46.0 | 34500 | 1.6868 | 0.8667 |
| 0.0 | 47.0 | 35250 | 1.6803 | 0.87 |
| 0.0 | 48.0 | 36000 | 1.6872 | 0.8733 |
| 0.0 | 49.0 | 36750 | 1.6911 | 0.8717 |
| 0.0 | 50.0 | 37500 | 1.6842 | 0.8717 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2