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---
license: apache-2.0
base_model: gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: CGIAR-Crop-disease
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. -->
# CGIAR-Crop-disease
This model is a fine-tuned version of [gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease](https://huggingface.co/gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7438
- Accuracy: 0.6964
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0386 | 1.0 | 652 | 0.9385 | 0.5669 |
| 0.9619 | 2.0 | 1304 | 0.9422 | 0.5811 |
| 0.9193 | 3.0 | 1956 | 0.8806 | 0.6348 |
| 0.8876 | 4.0 | 2608 | 0.8703 | 0.6488 |
| 0.8777 | 5.0 | 3260 | 0.8361 | 0.6607 |
| 0.863 | 6.0 | 3912 | 0.8543 | 0.6417 |
| 0.8316 | 7.0 | 4564 | 0.8101 | 0.6607 |
| 0.8301 | 8.0 | 5216 | 0.8197 | 0.6609 |
| 0.8264 | 9.0 | 5868 | 0.8111 | 0.6720 |
| 0.8283 | 10.0 | 6520 | 0.8065 | 0.6669 |
| 0.816 | 11.0 | 7172 | 0.8115 | 0.6578 |
| 0.8263 | 12.0 | 7824 | 0.8029 | 0.6753 |
| 0.8017 | 13.0 | 8476 | 0.7929 | 0.6707 |
| 0.8005 | 14.0 | 9128 | 0.8025 | 0.6661 |
| 0.7989 | 15.0 | 9780 | 0.8153 | 0.6594 |
| 0.7961 | 16.0 | 10432 | 0.8033 | 0.6720 |
| 0.7769 | 17.0 | 11084 | 0.7879 | 0.6682 |
| 0.7757 | 18.0 | 11736 | 0.7868 | 0.6732 |
| 0.7713 | 19.0 | 12388 | 0.7773 | 0.6747 |
| 0.7638 | 20.0 | 13040 | 0.7678 | 0.6811 |
| 0.7645 | 21.0 | 13692 | 0.7826 | 0.6795 |
| 0.7497 | 22.0 | 14344 | 0.7931 | 0.6807 |
| 0.761 | 23.0 | 14996 | 0.7719 | 0.6820 |
| 0.7486 | 24.0 | 15648 | 0.7641 | 0.6895 |
| 0.7446 | 25.0 | 16300 | 0.7686 | 0.6832 |
| 0.7418 | 26.0 | 16952 | 0.7683 | 0.6904 |
| 0.7344 | 27.0 | 17604 | 0.7549 | 0.6895 |
| 0.7369 | 28.0 | 18256 | 0.7501 | 0.6891 |
| 0.7238 | 29.0 | 18908 | 0.7454 | 0.6933 |
| 0.7264 | 30.0 | 19560 | 0.7565 | 0.6876 |
| 0.7185 | 31.0 | 20212 | 0.7524 | 0.6880 |
| 0.7112 | 32.0 | 20864 | 0.7712 | 0.6807 |
| 0.7073 | 33.0 | 21516 | 0.7532 | 0.6897 |
| 0.7102 | 34.0 | 22168 | 0.7457 | 0.6960 |
| 0.7053 | 35.0 | 22820 | 0.7438 | 0.6964 |
| 0.6979 | 36.0 | 23472 | 0.7449 | 0.6933 |
| 0.6973 | 37.0 | 24124 | 0.7477 | 0.6929 |
| 0.6967 | 38.0 | 24776 | 0.7508 | 0.6926 |
| 0.6939 | 39.0 | 25428 | 0.7481 | 0.6933 |
| 0.6936 | 40.0 | 26080 | 0.7460 | 0.6968 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.0.0
- Datasets 2.16.1
- Tokenizers 0.15.0
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