metadata
base_model: MBZUAI/swiftformer-xs
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: SF-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: 0.822429906542056
SF-RHS-DA
This model is a fine-tuned version of MBZUAI/swiftformer-xs on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.5233
- Accuracy: 0.8224
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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.693 | 0.99 | 35 | 0.6927 | 0.6822 |
| 0.6919 | 2.0 | 71 | 0.6910 | 0.6916 |
| 0.6872 | 2.99 | 106 | 0.6821 | 0.6822 |
| 0.6613 | 4.0 | 142 | 0.6552 | 0.6542 |
| 0.6196 | 4.99 | 177 | 0.6403 | 0.6168 |
| 0.5695 | 6.0 | 213 | 0.6128 | 0.6449 |
| 0.5436 | 6.99 | 248 | 0.6765 | 0.5701 |
| 0.4836 | 8.0 | 284 | 0.6075 | 0.6542 |
| 0.4902 | 8.99 | 319 | 0.5788 | 0.6355 |
| 0.4759 | 10.0 | 355 | 0.5284 | 0.7196 |
| 0.4746 | 10.99 | 390 | 0.5532 | 0.6822 |
| 0.4067 | 12.0 | 426 | 0.5356 | 0.7383 |
| 0.4138 | 12.99 | 461 | 0.5042 | 0.7477 |
| 0.3752 | 14.0 | 497 | 0.5063 | 0.7383 |
| 0.4158 | 14.99 | 532 | 0.4952 | 0.7570 |
| 0.3646 | 16.0 | 568 | 0.5440 | 0.7383 |
| 0.3644 | 16.99 | 603 | 0.5146 | 0.7757 |
| 0.3411 | 18.0 | 639 | 0.5208 | 0.7757 |
| 0.3052 | 18.99 | 674 | 0.5785 | 0.7383 |
| 0.3398 | 20.0 | 710 | 0.5366 | 0.7383 |
| 0.3103 | 20.99 | 745 | 0.5751 | 0.7290 |
| 0.3168 | 22.0 | 781 | 0.5194 | 0.7664 |
| 0.2927 | 22.99 | 816 | 0.5008 | 0.7944 |
| 0.2874 | 24.0 | 852 | 0.5216 | 0.7944 |
| 0.3021 | 24.99 | 887 | 0.5695 | 0.7570 |
| 0.2978 | 26.0 | 923 | 0.5643 | 0.7570 |
| 0.2743 | 26.99 | 958 | 0.5767 | 0.7570 |
| 0.2753 | 28.0 | 994 | 0.5125 | 0.7664 |
| 0.2773 | 28.99 | 1029 | 0.5246 | 0.7664 |
| 0.2775 | 30.0 | 1065 | 0.5473 | 0.7850 |
| 0.268 | 30.99 | 1100 | 0.5286 | 0.7664 |
| 0.2586 | 32.0 | 1136 | 0.5233 | 0.7850 |
| 0.2458 | 32.99 | 1171 | 0.5451 | 0.7757 |
| 0.2524 | 34.0 | 1207 | 0.5268 | 0.7850 |
| 0.2438 | 34.99 | 1242 | 0.5228 | 0.7757 |
| 0.2429 | 36.0 | 1278 | 0.5391 | 0.7664 |
| 0.2689 | 36.99 | 1313 | 0.5237 | 0.7850 |
| 0.2362 | 38.0 | 1349 | 0.5561 | 0.7664 |
| 0.2656 | 38.99 | 1384 | 0.5233 | 0.8224 |
| 0.264 | 39.44 | 1400 | 0.5112 | 0.8037 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0