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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
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