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---
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: SWv2-DMAE-H-4-rp-clean-fix-U-40-Cross-5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8690476190476191
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SWv2-DMAE-H-4-rp-clean-fix-U-40-Cross-5
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4646
- Accuracy: 0.8690
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6088 | 0.98 | 12 | 1.6063 | 0.2024 |
| 1.6024 | 1.96 | 24 | 1.5854 | 0.2024 |
| 1.568 | 2.94 | 36 | 1.5534 | 0.2024 |
| 1.5001 | 4.0 | 49 | 1.4693 | 0.2024 |
| 1.3811 | 4.98 | 61 | 1.3256 | 0.3690 |
| 1.27 | 5.96 | 73 | 1.0978 | 0.5476 |
| 1.0887 | 6.94 | 85 | 0.8237 | 0.7381 |
| 0.937 | 8.0 | 98 | 0.7746 | 0.7143 |
| 0.811 | 8.98 | 110 | 0.5772 | 0.7976 |
| 0.7574 | 9.96 | 122 | 0.6164 | 0.7857 |
| 0.7118 | 10.94 | 134 | 0.6410 | 0.7976 |
| 0.6374 | 12.0 | 147 | 0.5243 | 0.8095 |
| 0.5958 | 12.98 | 159 | 0.4589 | 0.8095 |
| 0.5446 | 13.96 | 171 | 0.5288 | 0.7738 |
| 0.5348 | 14.94 | 183 | 0.4989 | 0.7619 |
| 0.464 | 16.0 | 196 | 0.5408 | 0.7857 |
| 0.4641 | 16.98 | 208 | 0.4609 | 0.7738 |
| 0.4471 | 17.96 | 220 | 0.4229 | 0.8333 |
| 0.4301 | 18.94 | 232 | 0.3962 | 0.8452 |
| 0.3862 | 20.0 | 245 | 0.4005 | 0.8452 |
| 0.3659 | 20.98 | 257 | 0.3873 | 0.8452 |
| 0.3488 | 21.96 | 269 | 0.4196 | 0.8333 |
| 0.3683 | 22.94 | 281 | 0.4299 | 0.8095 |
| 0.3477 | 24.0 | 294 | 0.4470 | 0.8214 |
| 0.3426 | 24.98 | 306 | 0.4478 | 0.8333 |
| 0.3 | 25.96 | 318 | 0.4604 | 0.8452 |
| 0.3138 | 26.94 | 330 | 0.4114 | 0.8571 |
| 0.2569 | 28.0 | 343 | 0.4640 | 0.8452 |
| 0.2894 | 28.98 | 355 | 0.5187 | 0.7976 |
| 0.2996 | 29.96 | 367 | 0.4617 | 0.8452 |
| 0.3046 | 30.94 | 379 | 0.4646 | 0.8690 |
| 0.2896 | 32.0 | 392 | 0.4492 | 0.8571 |
| 0.2548 | 32.98 | 404 | 0.4523 | 0.8571 |
| 0.2137 | 33.96 | 416 | 0.4764 | 0.8333 |
| 0.2246 | 34.94 | 428 | 0.4474 | 0.8571 |
| 0.2684 | 36.0 | 441 | 0.4495 | 0.8452 |
| 0.2413 | 36.98 | 453 | 0.4634 | 0.8452 |
| 0.2633 | 37.96 | 465 | 0.4558 | 0.8452 |
| 0.2518 | 38.94 | 477 | 0.4523 | 0.8452 |
| 0.2428 | 39.18 | 480 | 0.4523 | 0.8452 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
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