metadata
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_10x_deit_tiny_sgd_00001_fold2
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: 0.5074875207986689
smids_10x_deit_tiny_sgd_00001_fold2
This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.9487
- Accuracy: 0.5075
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 |
|---|---|---|---|---|
| 1.3101 | 1.0 | 750 | 1.2862 | 0.3394 |
| 1.2651 | 2.0 | 1500 | 1.2316 | 0.3494 |
| 1.2534 | 3.0 | 2250 | 1.1915 | 0.3461 |
| 1.2051 | 4.0 | 3000 | 1.1627 | 0.3627 |
| 1.1704 | 5.0 | 3750 | 1.1422 | 0.3644 |
| 1.1559 | 6.0 | 4500 | 1.1266 | 0.3727 |
| 1.1314 | 7.0 | 5250 | 1.1140 | 0.3827 |
| 1.0917 | 8.0 | 6000 | 1.1032 | 0.3844 |
| 1.0985 | 9.0 | 6750 | 1.0936 | 0.4193 |
| 1.0734 | 10.0 | 7500 | 1.0847 | 0.4243 |
| 1.0397 | 11.0 | 8250 | 1.0764 | 0.4293 |
| 1.0584 | 12.0 | 9000 | 1.0685 | 0.4409 |
| 1.0692 | 13.0 | 9750 | 1.0611 | 0.4426 |
| 1.0127 | 14.0 | 10500 | 1.0540 | 0.4542 |
| 1.0605 | 15.0 | 11250 | 1.0471 | 0.4542 |
| 1.0197 | 16.0 | 12000 | 1.0406 | 0.4626 |
| 1.0472 | 17.0 | 12750 | 1.0344 | 0.4659 |
| 0.9868 | 18.0 | 13500 | 1.0285 | 0.4709 |
| 1.0498 | 19.0 | 14250 | 1.0228 | 0.4725 |
| 0.9916 | 20.0 | 15000 | 1.0174 | 0.4742 |
| 1.0032 | 21.0 | 15750 | 1.0122 | 0.4792 |
| 1.0262 | 22.0 | 16500 | 1.0073 | 0.4792 |
| 0.9732 | 23.0 | 17250 | 1.0026 | 0.4792 |
| 0.9627 | 24.0 | 18000 | 0.9981 | 0.4875 |
| 0.9933 | 25.0 | 18750 | 0.9939 | 0.4892 |
| 0.9645 | 26.0 | 19500 | 0.9898 | 0.4942 |
| 0.9413 | 27.0 | 20250 | 0.9860 | 0.4942 |
| 0.9502 | 28.0 | 21000 | 0.9824 | 0.4925 |
| 0.9622 | 29.0 | 21750 | 0.9790 | 0.4958 |
| 0.9399 | 30.0 | 22500 | 0.9758 | 0.4975 |
| 0.9259 | 31.0 | 23250 | 0.9728 | 0.4992 |
| 0.9425 | 32.0 | 24000 | 0.9700 | 0.5008 |
| 0.9657 | 33.0 | 24750 | 0.9673 | 0.5042 |
| 0.9537 | 34.0 | 25500 | 0.9649 | 0.5058 |
| 0.9361 | 35.0 | 26250 | 0.9627 | 0.5075 |
| 0.934 | 36.0 | 27000 | 0.9606 | 0.5092 |
| 0.927 | 37.0 | 27750 | 0.9587 | 0.5092 |
| 0.9435 | 38.0 | 28500 | 0.9570 | 0.5092 |
| 0.9139 | 39.0 | 29250 | 0.9555 | 0.5092 |
| 0.9394 | 40.0 | 30000 | 0.9541 | 0.5075 |
| 0.9635 | 41.0 | 30750 | 0.9529 | 0.5075 |
| 0.9447 | 42.0 | 31500 | 0.9519 | 0.5075 |
| 0.9124 | 43.0 | 32250 | 0.9510 | 0.5075 |
| 0.9404 | 44.0 | 33000 | 0.9503 | 0.5075 |
| 0.9374 | 45.0 | 33750 | 0.9497 | 0.5075 |
| 0.9103 | 46.0 | 34500 | 0.9493 | 0.5075 |
| 0.9609 | 47.0 | 35250 | 0.9490 | 0.5075 |
| 0.9309 | 48.0 | 36000 | 0.9488 | 0.5075 |
| 0.9307 | 49.0 | 36750 | 0.9487 | 0.5075 |
| 0.9119 | 50.0 | 37500 | 0.9487 | 0.5075 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2