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_00001_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: 0.8933333333333333
smids_10x_beit_large_adamax_00001_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: 1.1774
- Accuracy: 0.8933
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: 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 |
|---|---|---|---|---|
| 0.174 | 1.0 | 750 | 0.3318 | 0.8817 |
| 0.0694 | 2.0 | 1500 | 0.3979 | 0.8833 |
| 0.0385 | 3.0 | 2250 | 0.6069 | 0.8817 |
| 0.0028 | 4.0 | 3000 | 0.7041 | 0.8767 |
| 0.0151 | 5.0 | 3750 | 0.8263 | 0.8783 |
| 0.017 | 6.0 | 4500 | 0.8468 | 0.8917 |
| 0.0004 | 7.0 | 5250 | 0.9156 | 0.8817 |
| 0.0149 | 8.0 | 6000 | 0.9947 | 0.8883 |
| 0.0019 | 9.0 | 6750 | 0.9986 | 0.8833 |
| 0.0 | 10.0 | 7500 | 1.0174 | 0.89 |
| 0.0002 | 11.0 | 8250 | 1.0347 | 0.8983 |
| 0.0006 | 12.0 | 9000 | 1.1212 | 0.8883 |
| 0.0007 | 13.0 | 9750 | 1.1145 | 0.9 |
| 0.002 | 14.0 | 10500 | 1.1511 | 0.895 |
| 0.0113 | 15.0 | 11250 | 1.1891 | 0.8833 |
| 0.0193 | 16.0 | 12000 | 1.1467 | 0.8833 |
| 0.0 | 17.0 | 12750 | 1.2067 | 0.8833 |
| 0.0 | 18.0 | 13500 | 1.1030 | 0.8917 |
| 0.0 | 19.0 | 14250 | 1.2269 | 0.8817 |
| 0.0 | 20.0 | 15000 | 1.2142 | 0.8983 |
| 0.0 | 21.0 | 15750 | 1.2333 | 0.8833 |
| 0.0 | 22.0 | 16500 | 1.2215 | 0.89 |
| 0.0 | 23.0 | 17250 | 1.1755 | 0.88 |
| 0.0001 | 24.0 | 18000 | 1.2025 | 0.89 |
| 0.0 | 25.0 | 18750 | 1.1234 | 0.8967 |
| 0.0 | 26.0 | 19500 | 1.1299 | 0.8933 |
| 0.0 | 27.0 | 20250 | 1.1278 | 0.8933 |
| 0.0 | 28.0 | 21000 | 1.1853 | 0.89 |
| 0.0 | 29.0 | 21750 | 1.1366 | 0.8967 |
| 0.0 | 30.0 | 22500 | 1.2109 | 0.8817 |
| 0.0 | 31.0 | 23250 | 1.2247 | 0.88 |
| 0.0124 | 32.0 | 24000 | 1.2057 | 0.885 |
| 0.0 | 33.0 | 24750 | 1.2082 | 0.8933 |
| 0.0 | 34.0 | 25500 | 1.1875 | 0.8933 |
| 0.0 | 35.0 | 26250 | 1.1823 | 0.8983 |
| 0.0 | 36.0 | 27000 | 1.1794 | 0.8883 |
| 0.0 | 37.0 | 27750 | 1.1760 | 0.8917 |
| 0.0 | 38.0 | 28500 | 1.1363 | 0.895 |
| 0.0 | 39.0 | 29250 | 1.1574 | 0.895 |
| 0.0 | 40.0 | 30000 | 1.1725 | 0.8933 |
| 0.0 | 41.0 | 30750 | 1.1844 | 0.8867 |
| 0.0 | 42.0 | 31500 | 1.1542 | 0.8933 |
| 0.0 | 43.0 | 32250 | 1.1472 | 0.895 |
| 0.0 | 44.0 | 33000 | 1.1640 | 0.8917 |
| 0.0 | 45.0 | 33750 | 1.1642 | 0.89 |
| 0.0 | 46.0 | 34500 | 1.1680 | 0.8933 |
| 0.0 | 47.0 | 35250 | 1.1880 | 0.895 |
| 0.0 | 48.0 | 36000 | 1.1744 | 0.8933 |
| 0.0 | 49.0 | 36750 | 1.1763 | 0.8933 |
| 0.0008 | 50.0 | 37500 | 1.1774 | 0.8933 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2