---
library_name: transformers
license: other
base_model: google/mobilenet_v2_1.0_224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: mobilenet_v2_1.0_224-finetuned-plantdisease
  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: 0.9781976744186046
---

<!-- 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-finetuned-plantdisease

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: 0.0841
- Accuracy: 0.9782

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.7982        | 0.9983  | 145  | 1.9825          | 0.4036   |
| 0.6137        | 1.9966  | 290  | 1.1130          | 0.6415   |
| 0.4176        | 2.9948  | 435  | 0.4887          | 0.8469   |
| 0.3107        | 4.0     | 581  | 0.3414          | 0.8944   |
| 0.2255        | 4.9983  | 726  | 0.2732          | 0.9123   |
| 0.1833        | 5.9966  | 871  | 0.7462          | 0.7582   |
| 0.2062        | 6.9948  | 1016 | 0.3771          | 0.8803   |
| 0.1657        | 8.0     | 1162 | 0.4718          | 0.8542   |
| 0.1427        | 8.9983  | 1307 | 0.4902          | 0.8474   |
| 0.1598        | 9.9966  | 1452 | 0.2229          | 0.9273   |
| 0.1504        | 10.9948 | 1597 | 0.3021          | 0.8973   |
| 0.1456        | 12.0    | 1743 | 0.2422          | 0.9225   |
| 0.119         | 12.9983 | 1888 | 0.2836          | 0.9021   |
| 0.114         | 13.9966 | 2033 | 0.2038          | 0.9293   |
| 0.1378        | 14.9948 | 2178 | 0.2173          | 0.9239   |
| 0.1249        | 16.0    | 2324 | 0.2467          | 0.9186   |
| 0.1504        | 16.9983 | 2469 | 0.2322          | 0.9254   |
| 0.0972        | 17.9966 | 2614 | 0.0841          | 0.9782   |
| 0.1293        | 18.9948 | 2759 | 0.1512          | 0.9467   |
| 0.1072        | 19.9656 | 2900 | 0.1663          | 0.9448   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1