metadata
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_deit_base_adamax_001_fold5
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: 0.8866666666666667
smids_3x_deit_base_adamax_001_fold5
This model is a fine-tuned version of facebook/deit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.0193
- Accuracy: 0.8867
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 |
---|---|---|---|---|
0.3633 | 1.0 | 225 | 0.3986 | 0.8483 |
0.2906 | 2.0 | 450 | 0.3096 | 0.8867 |
0.2237 | 3.0 | 675 | 0.4059 | 0.8517 |
0.2184 | 4.0 | 900 | 0.3380 | 0.8917 |
0.1738 | 5.0 | 1125 | 0.3923 | 0.8833 |
0.0994 | 6.0 | 1350 | 0.4783 | 0.8667 |
0.1643 | 7.0 | 1575 | 0.3825 | 0.8983 |
0.1204 | 8.0 | 1800 | 0.4481 | 0.8683 |
0.0708 | 9.0 | 2025 | 0.4702 | 0.8883 |
0.0392 | 10.0 | 2250 | 0.5947 | 0.9017 |
0.0581 | 11.0 | 2475 | 0.5317 | 0.89 |
0.102 | 12.0 | 2700 | 0.6171 | 0.8683 |
0.0149 | 13.0 | 2925 | 0.4983 | 0.9 |
0.0251 | 14.0 | 3150 | 0.5396 | 0.8983 |
0.0063 | 15.0 | 3375 | 0.6932 | 0.8833 |
0.0424 | 16.0 | 3600 | 0.8036 | 0.865 |
0.0007 | 17.0 | 3825 | 0.7423 | 0.8817 |
0.022 | 18.0 | 4050 | 0.6506 | 0.8817 |
0.0402 | 19.0 | 4275 | 0.6999 | 0.89 |
0.0268 | 20.0 | 4500 | 0.8126 | 0.8883 |
0.0 | 21.0 | 4725 | 0.8105 | 0.9 |
0.0173 | 22.0 | 4950 | 0.8126 | 0.885 |
0.0167 | 23.0 | 5175 | 0.7462 | 0.8833 |
0.0056 | 24.0 | 5400 | 0.7445 | 0.8917 |
0.0002 | 25.0 | 5625 | 0.8258 | 0.8967 |
0.0018 | 26.0 | 5850 | 0.7747 | 0.8833 |
0.0003 | 27.0 | 6075 | 0.8895 | 0.89 |
0.0019 | 28.0 | 6300 | 0.8581 | 0.89 |
0.0039 | 29.0 | 6525 | 0.8693 | 0.8917 |
0.0 | 30.0 | 6750 | 0.9655 | 0.8867 |
0.0 | 31.0 | 6975 | 0.8077 | 0.8867 |
0.0 | 32.0 | 7200 | 0.8704 | 0.88 |
0.003 | 33.0 | 7425 | 0.8926 | 0.8933 |
0.0 | 34.0 | 7650 | 0.9137 | 0.8917 |
0.0029 | 35.0 | 7875 | 0.9309 | 0.89 |
0.0 | 36.0 | 8100 | 0.9596 | 0.8817 |
0.0036 | 37.0 | 8325 | 0.9111 | 0.8817 |
0.0009 | 38.0 | 8550 | 0.9317 | 0.8817 |
0.0 | 39.0 | 8775 | 0.9642 | 0.88 |
0.0 | 40.0 | 9000 | 0.9829 | 0.8817 |
0.0 | 41.0 | 9225 | 0.9951 | 0.8817 |
0.0 | 42.0 | 9450 | 1.0003 | 0.8817 |
0.0029 | 43.0 | 9675 | 0.9978 | 0.8833 |
0.0 | 44.0 | 9900 | 0.9820 | 0.8867 |
0.0 | 45.0 | 10125 | 0.9878 | 0.89 |
0.0 | 46.0 | 10350 | 1.0055 | 0.8883 |
0.0 | 47.0 | 10575 | 1.0104 | 0.8867 |
0.0 | 48.0 | 10800 | 1.0144 | 0.8867 |
0.0 | 49.0 | 11025 | 1.0175 | 0.8867 |
0.0 | 50.0 | 11250 | 1.0193 | 0.8867 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2