---
license: other
base_model: google/mobilenet_v2_1.0_224
tags:
- generated_from_trainer
datasets:
- A2H0H0R1/plant-disease
metrics:
- accuracy
model-index:
- name: mobilenet_v2_1.0_224-plant-disease2
  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: 0.9638691322901849
---

<!-- 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-disease2

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1510
- Accuracy: 0.9639

## 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: 5e-05
- train_batch_size: 100
- eval_batch_size: 100
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 400
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2171        | 1.0   | 158  | 1.0595          | 0.8188   |
| 0.4082        | 2.0   | 316  | 0.3154          | 0.9387   |
| 0.295         | 3.0   | 474  | 0.2191          | 0.9555   |
| 0.2266        | 4.0   | 633  | 0.1747          | 0.9595   |
| 0.2168        | 5.0   | 791  | 0.2135          | 0.9499   |
| 0.2091        | 5.99  | 948  | 0.1510          | 0.9639   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0