---
license: other
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: mobilenet_v2_1.0_224-plant-disease-identification
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: image_folder
      type: image_folder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7857752489331437
---

<!-- 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. -->

# mobilenet_v2_1.0_224-plant-disease-identification

This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) on the [Kaggle version](https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset) of the [Plant Village dataset](https://github.com/spMohanty/PlantVillage-Dataset).
It achieves the following results on the evaluation set:
- Cross Entropy Loss: 1.0461
- Accuracy: 0.7858

Will be further training it (such as finding optimal hyperparameters) better to achieve much better accuracy.

## Intended uses & limitations

For identifying common diseases in crops and assessing plant health.

## Training and evaluation data

The plant village dataset consists of 38 classes of diseases in common crops (including healthy/normal crops).

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.15
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.9265        | 1.0   | 248  | 2.7159          | 0.4703   |
| 1.9734        | 2.0   | 496  | 1.7668          | 0.6649   |
| 1.7206        | 3.0   | 744  | 1.4012          | 0.7206   |
| 1.6406        | 4.0   | 992  | 1.2514          | 0.7644   |
| 1.6075        | 5.0   | 1240 | 1.2934          | 0.7094   |
| 1.5932        | 6.0   | 1488 | 1.2093          | 0.7257   |
| 1.5203        | 7.0   | 1736 | 1.0461          | 0.7858   |
| 1.5076        | 8.0   | 1984 | 1.0580          | 0.7848   |


### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2