metadata
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_adamax_0001_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: 0.8733333333333333
smids_3x_beit_base_adamax_0001_fold4
This model is a fine-tuned version of microsoft/beit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.2272
- Accuracy: 0.8733
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: 0.0001
- 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 |
|---|---|---|---|---|
| 0.3102 | 1.0 | 225 | 0.3679 | 0.8633 |
| 0.1053 | 2.0 | 450 | 0.3867 | 0.87 |
| 0.0809 | 3.0 | 675 | 0.4978 | 0.8617 |
| 0.1418 | 4.0 | 900 | 0.5585 | 0.8717 |
| 0.0152 | 5.0 | 1125 | 0.6419 | 0.885 |
| 0.0232 | 6.0 | 1350 | 0.6902 | 0.8717 |
| 0.0119 | 7.0 | 1575 | 0.8503 | 0.8633 |
| 0.0116 | 8.0 | 1800 | 0.8413 | 0.8667 |
| 0.0484 | 9.0 | 2025 | 0.9018 | 0.8683 |
| 0.0101 | 10.0 | 2250 | 0.9930 | 0.855 |
| 0.0039 | 11.0 | 2475 | 1.0769 | 0.8733 |
| 0.0004 | 12.0 | 2700 | 1.0602 | 0.8717 |
| 0.0292 | 13.0 | 2925 | 1.1584 | 0.875 |
| 0.0029 | 14.0 | 3150 | 1.2271 | 0.8583 |
| 0.01 | 15.0 | 3375 | 1.1632 | 0.8733 |
| 0.0001 | 16.0 | 3600 | 1.1832 | 0.8633 |
| 0.0 | 17.0 | 3825 | 1.2281 | 0.86 |
| 0.004 | 18.0 | 4050 | 1.0844 | 0.8783 |
| 0.0003 | 19.0 | 4275 | 1.2463 | 0.8683 |
| 0.0112 | 20.0 | 4500 | 1.2122 | 0.8733 |
| 0.0013 | 21.0 | 4725 | 1.2444 | 0.8617 |
| 0.0002 | 22.0 | 4950 | 1.2159 | 0.86 |
| 0.0002 | 23.0 | 5175 | 1.2215 | 0.8667 |
| 0.0 | 24.0 | 5400 | 1.2014 | 0.8733 |
| 0.0007 | 25.0 | 5625 | 1.1844 | 0.875 |
| 0.0 | 26.0 | 5850 | 1.3054 | 0.8683 |
| 0.0 | 27.0 | 6075 | 1.3588 | 0.8583 |
| 0.0332 | 28.0 | 6300 | 1.2029 | 0.875 |
| 0.0 | 29.0 | 6525 | 1.2414 | 0.87 |
| 0.0001 | 30.0 | 6750 | 1.2400 | 0.8783 |
| 0.0005 | 31.0 | 6975 | 1.1861 | 0.87 |
| 0.0 | 32.0 | 7200 | 1.1528 | 0.8767 |
| 0.0 | 33.0 | 7425 | 1.2071 | 0.8783 |
| 0.0002 | 34.0 | 7650 | 1.2652 | 0.875 |
| 0.0 | 35.0 | 7875 | 1.2647 | 0.8783 |
| 0.0 | 36.0 | 8100 | 1.3389 | 0.865 |
| 0.0 | 37.0 | 8325 | 1.3158 | 0.8683 |
| 0.0 | 38.0 | 8550 | 1.2845 | 0.8717 |
| 0.0 | 39.0 | 8775 | 1.2211 | 0.8783 |
| 0.0383 | 40.0 | 9000 | 1.3005 | 0.865 |
| 0.0001 | 41.0 | 9225 | 1.3129 | 0.8567 |
| 0.0025 | 42.0 | 9450 | 1.2924 | 0.865 |
| 0.0 | 43.0 | 9675 | 1.2393 | 0.8667 |
| 0.0021 | 44.0 | 9900 | 1.2861 | 0.87 |
| 0.0 | 45.0 | 10125 | 1.2626 | 0.8717 |
| 0.0 | 46.0 | 10350 | 1.2383 | 0.8733 |
| 0.0 | 47.0 | 10575 | 1.2652 | 0.8717 |
| 0.0 | 48.0 | 10800 | 1.2466 | 0.8733 |
| 0.0 | 49.0 | 11025 | 1.2259 | 0.875 |
| 0.0 | 50.0 | 11250 | 1.2272 | 0.8733 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2