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---
library_name: transformers
license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swin-base-patch4-window7-224_rice-leaf-disease-augmented-v4_v5_pft
results: []
---
<!-- 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_rice-leaf-disease-augmented-v4_v5_pft
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4024
- Accuracy: 0.8490
## 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: 0.0003
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 256
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0846 | 0.5 | 64 | 1.9602 | 0.2483 |
| 1.7504 | 1.0 | 128 | 1.5308 | 0.5034 |
| 1.3704 | 1.5 | 192 | 1.1825 | 0.6107 |
| 1.113 | 2.0 | 256 | 0.9313 | 0.7148 |
| 0.9305 | 2.5 | 320 | 0.8132 | 0.7617 |
| 0.8171 | 3.0 | 384 | 0.7214 | 0.7651 |
| 0.7497 | 3.5 | 448 | 0.6650 | 0.7785 |
| 0.7039 | 4.0 | 512 | 0.6244 | 0.8188 |
| 0.6696 | 4.5 | 576 | 0.6003 | 0.8188 |
| 0.649 | 5.0 | 640 | 0.5976 | 0.8121 |
| 0.6334 | 5.5 | 704 | 0.6032 | 0.8020 |
| 0.6256 | 6.0 | 768 | 0.5859 | 0.8188 |
| 0.6417 | 6.5 | 832 | 0.5851 | 0.8188 |
| 0.5991 | 7.0 | 896 | 0.5835 | 0.8154 |
| 0.6014 | 7.5 | 960 | 0.5394 | 0.8322 |
| 0.5614 | 8.0 | 1024 | 0.5211 | 0.8356 |
| 0.536 | 8.5 | 1088 | 0.5184 | 0.8121 |
| 0.5443 | 9.0 | 1152 | 0.5256 | 0.8154 |
| 0.5129 | 9.5 | 1216 | 0.5026 | 0.8221 |
| 0.5084 | 10.0 | 1280 | 0.5028 | 0.8188 |
| 0.5081 | 10.5 | 1344 | 0.4996 | 0.8188 |
| 0.4936 | 11.0 | 1408 | 0.5004 | 0.8188 |
| 0.5 | 11.5 | 1472 | 0.5091 | 0.8121 |
| 0.4934 | 12.0 | 1536 | 0.4892 | 0.8356 |
| 0.4831 | 12.5 | 1600 | 0.4736 | 0.8322 |
| 0.4638 | 13.0 | 1664 | 0.4727 | 0.8255 |
| 0.4549 | 13.5 | 1728 | 0.4552 | 0.8456 |
| 0.4454 | 14.0 | 1792 | 0.4646 | 0.8322 |
| 0.44 | 14.5 | 1856 | 0.4610 | 0.8322 |
| 0.4304 | 15.0 | 1920 | 0.4574 | 0.8356 |
| 0.4255 | 15.5 | 1984 | 0.4550 | 0.8356 |
| 0.4353 | 16.0 | 2048 | 0.4548 | 0.8356 |
| 0.4456 | 16.5 | 2112 | 0.4465 | 0.8322 |
| 0.4047 | 17.0 | 2176 | 0.4619 | 0.8255 |
| 0.4119 | 17.5 | 2240 | 0.4497 | 0.8389 |
| 0.4009 | 18.0 | 2304 | 0.4329 | 0.8423 |
| 0.3901 | 18.5 | 2368 | 0.4286 | 0.8456 |
| 0.3936 | 19.0 | 2432 | 0.4318 | 0.8456 |
| 0.3761 | 19.5 | 2496 | 0.4297 | 0.8456 |
| 0.3885 | 20.0 | 2560 | 0.4279 | 0.8456 |
| 0.3806 | 20.5 | 2624 | 0.4271 | 0.8456 |
| 0.3779 | 21.0 | 2688 | 0.4352 | 0.8523 |
| 0.3746 | 21.5 | 2752 | 0.4256 | 0.8490 |
| 0.3708 | 22.0 | 2816 | 0.4253 | 0.8557 |
| 0.362 | 22.5 | 2880 | 0.4205 | 0.8490 |
| 0.3558 | 23.0 | 2944 | 0.4122 | 0.8490 |
| 0.3507 | 23.5 | 3008 | 0.4147 | 0.8423 |
| 0.3481 | 24.0 | 3072 | 0.4134 | 0.8456 |
| 0.3452 | 24.5 | 3136 | 0.4120 | 0.8456 |
| 0.3437 | 25.0 | 3200 | 0.4117 | 0.8490 |
| 0.3499 | 25.5 | 3264 | 0.4164 | 0.8523 |
| 0.3373 | 26.0 | 3328 | 0.4109 | 0.8490 |
| 0.3468 | 26.5 | 3392 | 0.3999 | 0.8523 |
| 0.3297 | 27.0 | 3456 | 0.4079 | 0.8523 |
| 0.329 | 27.5 | 3520 | 0.3997 | 0.8423 |
| 0.3293 | 28.0 | 3584 | 0.4051 | 0.8423 |
| 0.3147 | 28.5 | 3648 | 0.3987 | 0.8523 |
| 0.3239 | 29.0 | 3712 | 0.4013 | 0.8523 |
| 0.3147 | 29.5 | 3776 | 0.4031 | 0.8490 |
| 0.3167 | 30.0 | 3840 | 0.4024 | 0.8490 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.1
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