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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: swinv2-tiny-patch4-window8-256-DMAE-ex
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: 0.45652173913043476
---
<!-- 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. -->
# swinv2-tiny-patch4-window8-256-DMAE-ex
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: 1.2148
- Accuracy: 0.4565
## 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: 0.004
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.86 | 3 | 6.6832 | 0.1087 |
| No log | 2.0 | 7 | 1.2148 | 0.4565 |
| 4.4686 | 2.86 | 10 | 2.5061 | 0.3261 |
| 4.4686 | 4.0 | 14 | 1.4142 | 0.4565 |
| 4.4686 | 4.86 | 17 | 1.6118 | 0.4565 |
| 1.7414 | 6.0 | 21 | 1.2484 | 0.4565 |
| 1.7414 | 6.86 | 24 | 1.3690 | 0.3261 |
| 1.7414 | 8.0 | 28 | 1.4065 | 0.4565 |
| 1.3568 | 8.86 | 31 | 1.2682 | 0.3261 |
| 1.3568 | 10.0 | 35 | 1.2140 | 0.4565 |
| 1.3568 | 10.86 | 38 | 1.2591 | 0.4565 |
| 1.2275 | 12.0 | 42 | 1.2519 | 0.4565 |
| 1.2275 | 12.86 | 45 | 1.2184 | 0.4565 |
| 1.2275 | 14.0 | 49 | 1.2592 | 0.4565 |
| 1.3025 | 14.86 | 52 | 1.2246 | 0.4565 |
| 1.3025 | 16.0 | 56 | 1.3046 | 0.4565 |
| 1.3025 | 16.86 | 59 | 1.2177 | 0.4565 |
| 1.2981 | 18.0 | 63 | 1.2339 | 0.4565 |
| 1.2981 | 18.86 | 66 | 1.3139 | 0.4565 |
| 1.2765 | 20.0 | 70 | 1.2116 | 0.4565 |
| 1.2765 | 20.86 | 73 | 1.2284 | 0.3261 |
| 1.2765 | 22.0 | 77 | 1.2246 | 0.4565 |
| 1.2074 | 22.86 | 80 | 1.2631 | 0.4565 |
| 1.2074 | 24.0 | 84 | 1.2092 | 0.4565 |
| 1.2074 | 24.86 | 87 | 1.2147 | 0.4565 |
| 1.2048 | 26.0 | 91 | 1.2121 | 0.4565 |
| 1.2048 | 26.86 | 94 | 1.2156 | 0.4565 |
| 1.2048 | 28.0 | 98 | 1.2249 | 0.4565 |
| 1.2068 | 28.86 | 101 | 1.2159 | 0.4565 |
| 1.2068 | 30.0 | 105 | 1.2108 | 0.4565 |
| 1.2068 | 30.86 | 108 | 1.2116 | 0.4565 |
| 1.1961 | 32.0 | 112 | 1.2078 | 0.4565 |
| 1.1961 | 32.86 | 115 | 1.2070 | 0.4565 |
| 1.1961 | 34.0 | 119 | 1.2072 | 0.4565 |
| 1.1999 | 34.29 | 120 | 1.2072 | 0.4565 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
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