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_rms_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: 0.81
smids_3x_beit_base_rms_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: 1.8372
- Accuracy: 0.81
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 |
---|---|---|---|---|
0.787 | 1.0 | 225 | 0.7726 | 0.5917 |
0.7085 | 2.0 | 450 | 0.7059 | 0.6467 |
0.6145 | 3.0 | 675 | 0.6591 | 0.6983 |
0.6005 | 4.0 | 900 | 0.5422 | 0.7817 |
0.572 | 5.0 | 1125 | 0.5970 | 0.7567 |
0.4372 | 6.0 | 1350 | 0.5610 | 0.785 |
0.3918 | 7.0 | 1575 | 0.5957 | 0.7917 |
0.4058 | 8.0 | 1800 | 0.5296 | 0.7933 |
0.3971 | 9.0 | 2025 | 0.6041 | 0.7833 |
0.3274 | 10.0 | 2250 | 0.5347 | 0.8 |
0.2417 | 11.0 | 2475 | 0.6768 | 0.785 |
0.1989 | 12.0 | 2700 | 0.6501 | 0.8133 |
0.2222 | 13.0 | 2925 | 0.6337 | 0.7933 |
0.1654 | 14.0 | 3150 | 0.7865 | 0.7867 |
0.1241 | 15.0 | 3375 | 0.7840 | 0.8033 |
0.1208 | 16.0 | 3600 | 0.9856 | 0.795 |
0.0877 | 17.0 | 3825 | 1.0442 | 0.7767 |
0.1165 | 18.0 | 4050 | 0.9465 | 0.8117 |
0.1328 | 19.0 | 4275 | 0.8299 | 0.81 |
0.0427 | 20.0 | 4500 | 1.1880 | 0.7917 |
0.0826 | 21.0 | 4725 | 1.0665 | 0.8083 |
0.0679 | 22.0 | 4950 | 1.2201 | 0.7917 |
0.1018 | 23.0 | 5175 | 1.1824 | 0.8 |
0.0255 | 24.0 | 5400 | 1.2359 | 0.8117 |
0.0956 | 25.0 | 5625 | 1.2156 | 0.805 |
0.0725 | 26.0 | 5850 | 1.3671 | 0.81 |
0.0849 | 27.0 | 6075 | 1.3399 | 0.7917 |
0.068 | 28.0 | 6300 | 1.3279 | 0.8117 |
0.0512 | 29.0 | 6525 | 1.1460 | 0.82 |
0.0439 | 30.0 | 6750 | 1.4730 | 0.8017 |
0.0414 | 31.0 | 6975 | 1.2224 | 0.8067 |
0.0174 | 32.0 | 7200 | 1.6967 | 0.7983 |
0.0407 | 33.0 | 7425 | 1.5401 | 0.7983 |
0.0316 | 34.0 | 7650 | 1.2844 | 0.8017 |
0.0008 | 35.0 | 7875 | 1.7477 | 0.805 |
0.0104 | 36.0 | 8100 | 1.5173 | 0.8167 |
0.0005 | 37.0 | 8325 | 1.6340 | 0.7967 |
0.0286 | 38.0 | 8550 | 1.4323 | 0.7983 |
0.0292 | 39.0 | 8775 | 1.4953 | 0.805 |
0.0108 | 40.0 | 9000 | 1.6930 | 0.8183 |
0.022 | 41.0 | 9225 | 1.7083 | 0.8033 |
0.0101 | 42.0 | 9450 | 1.8030 | 0.8083 |
0.0122 | 43.0 | 9675 | 1.8925 | 0.8133 |
0.0071 | 44.0 | 9900 | 1.7250 | 0.815 |
0.0004 | 45.0 | 10125 | 1.7937 | 0.8017 |
0.0008 | 46.0 | 10350 | 1.9056 | 0.8067 |
0.0003 | 47.0 | 10575 | 1.8311 | 0.8083 |
0.0001 | 48.0 | 10800 | 1.9401 | 0.8033 |
0.0001 | 49.0 | 11025 | 1.8499 | 0.8083 |
0.0 | 50.0 | 11250 | 1.8372 | 0.81 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2