metadata
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_1x_beit_base_adamax_001_fold3
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: 0.8066666666666666
smids_1x_beit_base_adamax_001_fold3
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: 1.6207
- Accuracy: 0.8067
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 |
---|---|---|---|---|
0.961 | 1.0 | 75 | 0.9579 | 0.5083 |
0.8519 | 2.0 | 150 | 0.8223 | 0.555 |
0.8429 | 3.0 | 225 | 0.8258 | 0.5417 |
0.8689 | 4.0 | 300 | 1.1933 | 0.5183 |
0.7212 | 5.0 | 375 | 0.6887 | 0.7133 |
0.649 | 6.0 | 450 | 0.7128 | 0.6567 |
0.6409 | 7.0 | 525 | 0.6763 | 0.71 |
0.5869 | 8.0 | 600 | 0.5948 | 0.7383 |
0.5565 | 9.0 | 675 | 0.6418 | 0.695 |
0.5839 | 10.0 | 750 | 0.6087 | 0.7267 |
0.5293 | 11.0 | 825 | 0.5977 | 0.7267 |
0.4762 | 12.0 | 900 | 0.5491 | 0.7783 |
0.4499 | 13.0 | 975 | 0.5838 | 0.7517 |
0.4302 | 14.0 | 1050 | 0.5473 | 0.77 |
0.4099 | 15.0 | 1125 | 0.5508 | 0.755 |
0.3178 | 16.0 | 1200 | 0.5699 | 0.78 |
0.341 | 17.0 | 1275 | 0.6033 | 0.7933 |
0.2555 | 18.0 | 1350 | 0.6573 | 0.7767 |
0.3366 | 19.0 | 1425 | 0.5611 | 0.7933 |
0.1724 | 20.0 | 1500 | 0.7339 | 0.7933 |
0.2297 | 21.0 | 1575 | 0.8132 | 0.78 |
0.2293 | 22.0 | 1650 | 0.7112 | 0.7833 |
0.1656 | 23.0 | 1725 | 0.8681 | 0.7767 |
0.1488 | 24.0 | 1800 | 0.9454 | 0.79 |
0.1667 | 25.0 | 1875 | 0.9934 | 0.7767 |
0.0534 | 26.0 | 1950 | 0.9484 | 0.7767 |
0.1635 | 27.0 | 2025 | 1.0833 | 0.77 |
0.0554 | 28.0 | 2100 | 1.1552 | 0.8017 |
0.0938 | 29.0 | 2175 | 1.0865 | 0.7917 |
0.1141 | 30.0 | 2250 | 1.3605 | 0.7883 |
0.0561 | 31.0 | 2325 | 1.2003 | 0.8033 |
0.064 | 32.0 | 2400 | 1.3257 | 0.7933 |
0.0695 | 33.0 | 2475 | 1.6036 | 0.7883 |
0.0143 | 34.0 | 2550 | 1.5166 | 0.7717 |
0.0099 | 35.0 | 2625 | 1.5177 | 0.7833 |
0.046 | 36.0 | 2700 | 1.6809 | 0.7983 |
0.0535 | 37.0 | 2775 | 1.6548 | 0.7783 |
0.0142 | 38.0 | 2850 | 1.9052 | 0.7867 |
0.0043 | 39.0 | 2925 | 1.8855 | 0.785 |
0.0169 | 40.0 | 3000 | 1.8422 | 0.7983 |
0.0085 | 41.0 | 3075 | 1.6803 | 0.8033 |
0.0125 | 42.0 | 3150 | 1.4852 | 0.8033 |
0.0037 | 43.0 | 3225 | 1.5490 | 0.7883 |
0.0153 | 44.0 | 3300 | 1.3985 | 0.81 |
0.0066 | 45.0 | 3375 | 1.5369 | 0.8083 |
0.0076 | 46.0 | 3450 | 1.5177 | 0.7983 |
0.0089 | 47.0 | 3525 | 1.6039 | 0.7883 |
0.0027 | 48.0 | 3600 | 1.6013 | 0.8067 |
0.0003 | 49.0 | 3675 | 1.6182 | 0.8067 |
0.0026 | 50.0 | 3750 | 1.6207 | 0.8067 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0