metadata
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_deit_small_sgd_001_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: 0.8569051580698835
smids_3x_deit_small_sgd_001_fold2
This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3562
- Accuracy: 0.8569
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 |
---|---|---|---|---|
0.8599 | 1.0 | 225 | 0.8308 | 0.6789 |
0.6572 | 2.0 | 450 | 0.6508 | 0.7488 |
0.5628 | 3.0 | 675 | 0.5574 | 0.7920 |
0.4888 | 4.0 | 900 | 0.5053 | 0.8053 |
0.4209 | 5.0 | 1125 | 0.4713 | 0.8136 |
0.3729 | 6.0 | 1350 | 0.4472 | 0.8253 |
0.4088 | 7.0 | 1575 | 0.4292 | 0.8270 |
0.3871 | 8.0 | 1800 | 0.4168 | 0.8353 |
0.3605 | 9.0 | 2025 | 0.4049 | 0.8369 |
0.2963 | 10.0 | 2250 | 0.3978 | 0.8403 |
0.3519 | 11.0 | 2475 | 0.3893 | 0.8469 |
0.3126 | 12.0 | 2700 | 0.3814 | 0.8536 |
0.2889 | 13.0 | 2925 | 0.3781 | 0.8519 |
0.3096 | 14.0 | 3150 | 0.3739 | 0.8552 |
0.3153 | 15.0 | 3375 | 0.3694 | 0.8586 |
0.3271 | 16.0 | 3600 | 0.3680 | 0.8619 |
0.2697 | 17.0 | 3825 | 0.3655 | 0.8602 |
0.2138 | 18.0 | 4050 | 0.3624 | 0.8602 |
0.2422 | 19.0 | 4275 | 0.3602 | 0.8636 |
0.288 | 20.0 | 4500 | 0.3609 | 0.8652 |
0.3039 | 21.0 | 4725 | 0.3587 | 0.8619 |
0.2907 | 22.0 | 4950 | 0.3580 | 0.8619 |
0.3138 | 23.0 | 5175 | 0.3576 | 0.8652 |
0.2718 | 24.0 | 5400 | 0.3569 | 0.8669 |
0.263 | 25.0 | 5625 | 0.3551 | 0.8669 |
0.245 | 26.0 | 5850 | 0.3538 | 0.8686 |
0.2019 | 27.0 | 6075 | 0.3530 | 0.8636 |
0.2353 | 28.0 | 6300 | 0.3539 | 0.8636 |
0.2451 | 29.0 | 6525 | 0.3558 | 0.8602 |
0.2565 | 30.0 | 6750 | 0.3536 | 0.8652 |
0.2202 | 31.0 | 6975 | 0.3542 | 0.8636 |
0.2433 | 32.0 | 7200 | 0.3552 | 0.8636 |
0.2621 | 33.0 | 7425 | 0.3534 | 0.8652 |
0.2353 | 34.0 | 7650 | 0.3541 | 0.8652 |
0.1836 | 35.0 | 7875 | 0.3533 | 0.8602 |
0.2199 | 36.0 | 8100 | 0.3554 | 0.8619 |
0.2271 | 37.0 | 8325 | 0.3536 | 0.8602 |
0.1937 | 38.0 | 8550 | 0.3541 | 0.8619 |
0.1782 | 39.0 | 8775 | 0.3547 | 0.8586 |
0.1988 | 40.0 | 9000 | 0.3551 | 0.8586 |
0.1613 | 41.0 | 9225 | 0.3546 | 0.8602 |
0.1997 | 42.0 | 9450 | 0.3550 | 0.8586 |
0.1938 | 43.0 | 9675 | 0.3554 | 0.8569 |
0.1972 | 44.0 | 9900 | 0.3557 | 0.8586 |
0.2519 | 45.0 | 10125 | 0.3555 | 0.8569 |
0.2003 | 46.0 | 10350 | 0.3557 | 0.8569 |
0.1846 | 47.0 | 10575 | 0.3560 | 0.8586 |
0.1808 | 48.0 | 10800 | 0.3561 | 0.8569 |
0.2273 | 49.0 | 11025 | 0.3561 | 0.8569 |
0.1583 | 50.0 | 11250 | 0.3562 | 0.8569 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2