---
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: 0.7448
- Accuracy: 0.6857

## 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: 30

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9931        | 1.0   | 652   | 0.8450          | 0.6346   |
| 0.9034        | 2.0   | 1304  | 0.8367          | 0.6456   |
| 0.8734        | 3.0   | 1956  | 0.8165          | 0.6601   |
| 0.851         | 4.0   | 2608  | 0.8982          | 0.6047   |
| 0.8444        | 5.0   | 3260  | 0.8000          | 0.6626   |
| 0.8261        | 6.0   | 3912  | 0.8339          | 0.6321   |
| 0.8262        | 7.0   | 4564  | 0.7984          | 0.6613   |
| 0.8152        | 8.0   | 5216  | 0.7859          | 0.6740   |
| 0.8081        | 9.0   | 5868  | 0.8387          | 0.6400   |
| 0.8012        | 10.0  | 6520  | 0.8229          | 0.6463   |
| 0.7957        | 11.0  | 7172  | 0.7807          | 0.6715   |
| 0.7975        | 12.0  | 7824  | 0.7752          | 0.6816   |
| 0.7885        | 13.0  | 8476  | 0.7885          | 0.6694   |
| 0.7896        | 14.0  | 9128  | 0.7806          | 0.6774   |
| 0.7871        | 15.0  | 9780  | 0.7713          | 0.6786   |
| 0.7696        | 16.0  | 10432 | 0.7881          | 0.6615   |
| 0.7742        | 17.0  | 11084 | 0.7616          | 0.6797   |
| 0.7638        | 18.0  | 11736 | 0.7509          | 0.6878   |
| 0.7655        | 19.0  | 12388 | 0.7995          | 0.6646   |
| 0.7624        | 20.0  | 13040 | 0.7712          | 0.6768   |
| 0.7544        | 21.0  | 13692 | 0.7491          | 0.6885   |
| 0.7567        | 22.0  | 14344 | 0.7472          | 0.6841   |
| 0.7487        | 23.0  | 14996 | 0.7608          | 0.6818   |
| 0.7427        | 24.0  | 15648 | 0.7494          | 0.6870   |
| 0.7468        | 25.0  | 16300 | 0.7543          | 0.6812   |
| 0.7365        | 26.0  | 16952 | 0.7494          | 0.6855   |
| 0.7328        | 27.0  | 17604 | 0.7448          | 0.6857   |
| 0.7398        | 28.0  | 18256 | 0.7461          | 0.6855   |
| 0.7266        | 29.0  | 18908 | 0.7513          | 0.6822   |
| 0.7286        | 30.0  | 19560 | 0.7456          | 0.6868   |


### Framework versions

- Transformers 4.37.1
- Pytorch 2.0.0
- Datasets 2.16.1
- Tokenizers 0.15.0