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---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224_ft_mango_leaf_disease
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: 0.9986111111111111
---
<!-- 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-tiny-patch4-window7-224_ft_mango_leaf_disease
This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0089
- Accuracy: 0.9986
## Model description
Multiclass image classification model based on [swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) and fine-tuned with Mango🥭 Leaf🍃🍂 Disease Dataset.
Model was trained on 8 classes based on mango leaves health :
Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, Sooty Mould, Healthy
## Intended uses & limitations
More information needed
## Training and evaluation data
Traning and evaluation data are from this Kaggle dataset [Mango🥭 Leaf🍃🍂 Disease Dataset](https://www.kaggle.com/datasets/aryashah2k/mango-leaf-disease-dataset).
Amount of images used was 90% of total images (3600 of 4000, 450 images from each class).
## Training procedure
Dataset split : 75% train set, 20% validation set, 5% test set.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 143
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.93 | 10 | 0.1208 | 0.9931 |
| 0.1082 | 1.95 | 21 | 0.0551 | 0.9958 |
| 0.1082 | 2.98 | 32 | 0.0297 | 0.9958 |
| 0.0342 | 4.0 | 43 | 0.0189 | 0.9986 |
| 0.0342 | 4.93 | 53 | 0.0156 | 0.9972 |
| 0.0164 | 5.95 | 64 | 0.0122 | 0.9972 |
| 0.0164 | 6.98 | 75 | 0.0100 | 0.9986 |
| 0.0099 | 8.0 | 86 | 0.0096 | 0.9986 |
| 0.0099 | 8.93 | 96 | 0.0090 | 0.9986 |
| 0.0085 | 9.3 | 100 | 0.0089 | 0.9986 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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