metadata
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: SW2-RHS-DA
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8598130841121495
SW2-RHS-DA
This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.4541
- Accuracy: 0.8598
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: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.0538 | 0.99 | 35 | 1.2866 | 0.4112 |
| 0.7464 | 2.0 | 71 | 0.6780 | 0.5888 |
| 0.7061 | 2.99 | 106 | 0.6851 | 0.5888 |
| 0.6951 | 4.0 | 142 | 0.6742 | 0.5888 |
| 0.6928 | 4.99 | 177 | 0.6917 | 0.4486 |
| 0.683 | 6.0 | 213 | 0.6531 | 0.5794 |
| 0.7013 | 6.99 | 248 | 0.7000 | 0.4486 |
| 0.6921 | 8.0 | 284 | 0.7519 | 0.5514 |
| 0.6166 | 8.99 | 319 | 0.5947 | 0.6822 |
| 0.6128 | 10.0 | 355 | 0.5434 | 0.7850 |
| 0.5737 | 10.99 | 390 | 0.5533 | 0.7570 |
| 0.5376 | 12.0 | 426 | 0.5347 | 0.7103 |
| 0.5056 | 12.99 | 461 | 0.4949 | 0.7664 |
| 0.5396 | 14.0 | 497 | 0.5151 | 0.7477 |
| 0.4826 | 14.99 | 532 | 0.5669 | 0.7196 |
| 0.4269 | 16.0 | 568 | 0.4796 | 0.7570 |
| 0.5004 | 16.99 | 603 | 0.4489 | 0.8037 |
| 0.4116 | 18.0 | 639 | 0.4362 | 0.8224 |
| 0.3776 | 18.99 | 674 | 0.5300 | 0.7570 |
| 0.3646 | 20.0 | 710 | 0.4175 | 0.8037 |
| 0.3683 | 20.99 | 745 | 0.4700 | 0.8224 |
| 0.3277 | 22.0 | 781 | 0.4707 | 0.8131 |
| 0.3534 | 22.99 | 816 | 0.5240 | 0.8131 |
| 0.3083 | 24.0 | 852 | 0.5012 | 0.8131 |
| 0.2829 | 24.99 | 887 | 0.4421 | 0.8318 |
| 0.2564 | 26.0 | 923 | 0.4548 | 0.8224 |
| 0.3136 | 26.99 | 958 | 0.4374 | 0.8318 |
| 0.2443 | 28.0 | 994 | 0.5277 | 0.8131 |
| 0.258 | 28.99 | 1029 | 0.4601 | 0.8224 |
| 0.2673 | 30.0 | 1065 | 0.4520 | 0.8318 |
| 0.2233 | 30.99 | 1100 | 0.4541 | 0.8598 |
| 0.2276 | 32.0 | 1136 | 0.4247 | 0.8505 |
| 0.2653 | 32.99 | 1171 | 0.4091 | 0.8505 |
| 0.2007 | 34.0 | 1207 | 0.4719 | 0.8505 |
| 0.2082 | 34.99 | 1242 | 0.4624 | 0.8411 |
| 0.1794 | 36.0 | 1278 | 0.4856 | 0.8318 |
| 0.1987 | 36.99 | 1313 | 0.4904 | 0.8224 |
| 0.2066 | 38.0 | 1349 | 0.4741 | 0.8505 |
| 0.1972 | 38.99 | 1384 | 0.4530 | 0.8505 |
| 0.2319 | 39.44 | 1400 | 0.4536 | 0.8505 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0