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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-new_dataset_50e
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: 0.7972972972972973
---
<!-- 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-new_dataset_50e
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: 0.6407
- Accuracy: 0.7973
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- 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 | 0.94 | 4 | 0.7081 | 0.6081 |
| No log | 1.94 | 8 | 0.7104 | 0.6351 |
| 0.5516 | 2.94 | 12 | 0.6911 | 0.6351 |
| 0.5516 | 3.94 | 16 | 0.7156 | 0.7027 |
| 0.537 | 4.94 | 20 | 0.7345 | 0.7297 |
| 0.537 | 5.94 | 24 | 0.6745 | 0.6892 |
| 0.537 | 6.94 | 28 | 0.7146 | 0.7297 |
| 0.5333 | 7.94 | 32 | 0.7057 | 0.6892 |
| 0.5333 | 8.94 | 36 | 0.6531 | 0.7027 |
| 0.4871 | 9.94 | 40 | 0.6405 | 0.7027 |
| 0.4871 | 10.94 | 44 | 0.6126 | 0.6892 |
| 0.4871 | 11.94 | 48 | 0.6303 | 0.7027 |
| 0.4432 | 12.94 | 52 | 0.6264 | 0.7027 |
| 0.4432 | 13.94 | 56 | 0.6347 | 0.7432 |
| 0.3669 | 14.94 | 60 | 0.6698 | 0.6622 |
| 0.3669 | 15.94 | 64 | 0.6346 | 0.7568 |
| 0.3669 | 16.94 | 68 | 0.6510 | 0.6892 |
| 0.3704 | 17.94 | 72 | 0.6491 | 0.6892 |
| 0.3704 | 18.94 | 76 | 0.5947 | 0.7568 |
| 0.3624 | 19.94 | 80 | 0.6248 | 0.7027 |
| 0.3624 | 20.94 | 84 | 0.6580 | 0.7027 |
| 0.3624 | 21.94 | 88 | 0.6345 | 0.7162 |
| 0.3164 | 22.94 | 92 | 0.6092 | 0.7568 |
| 0.3164 | 23.94 | 96 | 0.6498 | 0.7162 |
| 0.2777 | 24.94 | 100 | 0.6915 | 0.7703 |
| 0.2777 | 25.94 | 104 | 0.6482 | 0.7838 |
| 0.2777 | 26.94 | 108 | 0.6407 | 0.7973 |
| 0.2946 | 27.94 | 112 | 0.6135 | 0.7838 |
| 0.2946 | 28.94 | 116 | 0.6819 | 0.7568 |
| 0.2546 | 29.94 | 120 | 0.6401 | 0.7568 |
| 0.2546 | 30.94 | 124 | 0.6370 | 0.7432 |
| 0.2546 | 31.94 | 128 | 0.6488 | 0.7703 |
| 0.2477 | 32.94 | 132 | 0.6429 | 0.7973 |
| 0.2477 | 33.94 | 136 | 0.6540 | 0.7703 |
| 0.1968 | 34.94 | 140 | 0.5895 | 0.7973 |
| 0.1968 | 35.94 | 144 | 0.6242 | 0.7568 |
| 0.1968 | 36.94 | 148 | 0.6575 | 0.7568 |
| 0.2235 | 37.94 | 152 | 0.6263 | 0.7703 |
| 0.2235 | 38.94 | 156 | 0.6225 | 0.7838 |
| 0.2005 | 39.94 | 160 | 0.6731 | 0.7703 |
| 0.2005 | 40.94 | 164 | 0.6844 | 0.7703 |
| 0.2005 | 41.94 | 168 | 0.6550 | 0.7703 |
| 0.2062 | 42.94 | 172 | 0.6700 | 0.7703 |
| 0.2062 | 43.94 | 176 | 0.6661 | 0.7703 |
| 0.1933 | 44.94 | 180 | 0.6606 | 0.7838 |
| 0.1933 | 45.94 | 184 | 0.6757 | 0.7703 |
| 0.1933 | 46.94 | 188 | 0.6889 | 0.7568 |
| 0.1895 | 47.94 | 192 | 0.6940 | 0.7568 |
| 0.1895 | 48.94 | 196 | 0.6919 | 0.7568 |
| 0.1666 | 49.94 | 200 | 0.6899 | 0.7432 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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