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---
license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224-in22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-base-patch4-window7-224-in22k-finetuned-cifar10
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: 0.9858
---
<!-- 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-base-patch4-window7-224-in22k-finetuned-cifar10
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0414
- Accuracy: 0.9858
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.303 | 0.03 | 10 | 2.1672 | 0.2334 |
| 2.0158 | 0.06 | 20 | 1.6672 | 0.657 |
| 1.4855 | 0.09 | 30 | 0.8292 | 0.8704 |
| 0.7451 | 0.11 | 40 | 0.2578 | 0.93 |
| 0.5618 | 0.14 | 50 | 0.1476 | 0.962 |
| 0.4545 | 0.17 | 60 | 0.1248 | 0.9642 |
| 0.4587 | 0.2 | 70 | 0.0941 | 0.9748 |
| 0.3911 | 0.23 | 80 | 0.0944 | 0.9712 |
| 0.3839 | 0.26 | 90 | 0.0848 | 0.9756 |
| 0.3864 | 0.28 | 100 | 0.0744 | 0.978 |
| 0.3141 | 0.31 | 110 | 0.0673 | 0.98 |
| 0.3764 | 0.34 | 120 | 0.0706 | 0.9764 |
| 0.3003 | 0.37 | 130 | 0.0600 | 0.984 |
| 0.3566 | 0.4 | 140 | 0.0562 | 0.9826 |
| 0.2855 | 0.43 | 150 | 0.0567 | 0.9816 |
| 0.3351 | 0.45 | 160 | 0.0543 | 0.9828 |
| 0.2977 | 0.48 | 170 | 0.0568 | 0.9798 |
| 0.2924 | 0.51 | 180 | 0.0577 | 0.9804 |
| 0.2884 | 0.54 | 190 | 0.0551 | 0.983 |
| 0.3067 | 0.57 | 200 | 0.0487 | 0.983 |
| 0.3159 | 0.6 | 210 | 0.0513 | 0.984 |
| 0.2795 | 0.63 | 220 | 0.0460 | 0.9846 |
| 0.3113 | 0.65 | 230 | 0.0495 | 0.9832 |
| 0.2882 | 0.68 | 240 | 0.0475 | 0.9838 |
| 0.263 | 0.71 | 250 | 0.0449 | 0.9854 |
| 0.2686 | 0.74 | 260 | 0.0510 | 0.9826 |
| 0.2705 | 0.77 | 270 | 0.0483 | 0.9846 |
| 0.2807 | 0.8 | 280 | 0.0430 | 0.9854 |
| 0.2583 | 0.82 | 290 | 0.0452 | 0.9858 |
| 0.2346 | 0.85 | 300 | 0.0435 | 0.9858 |
| 0.2294 | 0.88 | 310 | 0.0434 | 0.986 |
| 0.2608 | 0.91 | 320 | 0.0433 | 0.986 |
| 0.2642 | 0.94 | 330 | 0.0425 | 0.9866 |
| 0.2781 | 0.97 | 340 | 0.0417 | 0.986 |
| 0.247 | 1.0 | 350 | 0.0414 | 0.9858 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.1
- Datasets 2.14.6
- Tokenizers 0.14.1
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