---
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