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---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-wuhan
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: 1.0
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-wuhan
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co./microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 3 | 0.6245 | 0.7778 |
| No log | 2.0 | 6 | 0.5321 | 0.7778 |
| No log | 3.0 | 9 | 0.5123 | 0.7778 |
| 0.6482 | 4.0 | 12 | 0.4956 | 0.7778 |
| 0.6482 | 5.0 | 15 | 0.4585 | 0.7778 |
| 0.6482 | 6.0 | 18 | 0.3743 | 0.8611 |
| 0.5574 | 7.0 | 21 | 0.2842 | 0.9167 |
| 0.5574 | 8.0 | 24 | 0.2125 | 0.9167 |
| 0.5574 | 9.0 | 27 | 0.2683 | 0.9167 |
| 0.4882 | 10.0 | 30 | 0.1316 | 0.9444 |
| 0.4882 | 11.0 | 33 | 0.1366 | 0.9444 |
| 0.4882 | 12.0 | 36 | 0.0745 | 0.9722 |
| 0.4882 | 13.0 | 39 | 0.1065 | 0.9444 |
| 0.0907 | 14.0 | 42 | 0.0477 | 0.9722 |
| 0.0907 | 15.0 | 45 | 0.0460 | 0.9444 |
| 0.0907 | 16.0 | 48 | 0.0438 | 0.9722 |
| 0.0481 | 17.0 | 51 | 0.0203 | 1.0 |
| 0.0481 | 18.0 | 54 | 0.0093 | 1.0 |
| 0.0481 | 19.0 | 57 | 0.0082 | 1.0 |
| 0.013 | 20.0 | 60 | 0.0017 | 1.0 |
| 0.013 | 21.0 | 63 | 0.0008 | 1.0 |
| 0.013 | 22.0 | 66 | 0.0002 | 1.0 |
| 0.013 | 23.0 | 69 | 0.0001 | 1.0 |
| 0.0101 | 24.0 | 72 | 0.0938 | 0.9722 |
| 0.0101 | 25.0 | 75 | 0.1019 | 0.9722 |
| 0.0101 | 26.0 | 78 | 0.0005 | 1.0 |
| 0.0085 | 27.0 | 81 | 0.0000 | 1.0 |
| 0.0085 | 28.0 | 84 | 0.0000 | 1.0 |
| 0.0085 | 29.0 | 87 | 0.0001 | 1.0 |
| 0.0196 | 30.0 | 90 | 0.0001 | 1.0 |
| 0.0196 | 31.0 | 93 | 0.0001 | 1.0 |
| 0.0196 | 32.0 | 96 | 0.0000 | 1.0 |
| 0.0196 | 33.0 | 99 | 0.0000 | 1.0 |
| 0.0027 | 34.0 | 102 | 0.0000 | 1.0 |
| 0.0027 | 35.0 | 105 | 0.0000 | 1.0 |
| 0.0027 | 36.0 | 108 | 0.0000 | 1.0 |
| 0.0016 | 37.0 | 111 | 0.0000 | 1.0 |
| 0.0016 | 38.0 | 114 | 0.0000 | 1.0 |
| 0.0016 | 39.0 | 117 | 0.0000 | 1.0 |
| 0.0021 | 40.0 | 120 | 0.0000 | 1.0 |
| 0.0021 | 41.0 | 123 | 0.0000 | 1.0 |
| 0.0021 | 42.0 | 126 | 0.0000 | 1.0 |
| 0.0021 | 43.0 | 129 | 0.0000 | 1.0 |
| 0.0024 | 44.0 | 132 | 0.0000 | 1.0 |
| 0.0024 | 45.0 | 135 | 0.0000 | 1.0 |
| 0.0024 | 46.0 | 138 | 0.0000 | 1.0 |
| 0.0009 | 47.0 | 141 | 0.0000 | 1.0 |
| 0.0009 | 48.0 | 144 | 0.0000 | 1.0 |
| 0.0009 | 49.0 | 147 | 0.0000 | 1.0 |
| 0.0006 | 50.0 | 150 | 0.0000 | 1.0 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3
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