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# 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: 0.15
- Accuracy: 0.9541

## Intended uses & limitations

For identifying common diseases in crops and assessing plant health. Not to be used as a replacement for an actual diagnosis from experts.

## 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: 5e-5
- train_batch_size: 256
- eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 6

### Framework versions

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

{"license": "other", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "mobilenet_v2_1.0_224-plant-disease-identification", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "New Plant Diseases Dataset", "type": "image_folder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9541, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard mobilenet_v2 image-classification generated_from_trainer dataset:image_folder license:other model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #mobilenet_v2 #image-classification #generated_from_trainer #dataset-image_folder #license-other #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 




# mobilenet_v2_1.0_224-plant-disease-identification

This model is a fine-tuned version of google/mobilenet_v2_1.0_224 on the Kaggle version of the Plant Village dataset.
It achieves the following results on the evaluation set:
- Cross Entropy Loss: 0.15
- Accuracy: 0.9541

## Intended uses & limitations

For identifying common diseases in crops and assessing plant health. Not to be used as a replacement for an actual diagnosis from experts.

## 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: 5e-5
- train_batch_size: 256
- eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 6

### Framework versions

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

# mobilenet_v2_1.0_224-plant-disease-identification

This model is a fine-tuned version of google/mobilenet_v2_1.0_224 on the Kaggle version of the Plant Village dataset.
It achieves the following results on the evaluation set:
- Cross Entropy Loss: 0.15
- Accuracy: 0.9541 ## Intended uses & limitations

For identifying common diseases in crops and assessing plant health. Not to be used as a replacement for an actual diagnosis from experts. ## 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: 5e-5
- train_batch_size: 256
- eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 6 ### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mobilenet_v2 #image-classification #generated_from_trainer #dataset-image_folder #license-other #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # mobilenet_v2_1.0_224-plant-disease-identification

This model is a fine-tuned version of google/mobilenet_v2_1.0_224 on the Kaggle version of the Plant Village dataset.
It achieves the following results on the evaluation set:
- Cross Entropy Loss: 0.15
- Accuracy: 0.9541 ## Intended uses & limitations

For identifying common diseases in crops and assessing plant health. Not to be used as a replacement for an actual diagnosis from experts. ## 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: 5e-5
- train_batch_size: 256
- eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 6 ### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mobilenet_v2 #image-classification #generated_from_trainer #dataset-image_folder #license-other #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
# mobilenet_v2_1.0_224-plant-disease-identification

This model is a fine-tuned version of google/mobilenet_v2_1.0_224 on the Kaggle version of the Plant Village dataset.
It achieves the following results on the evaluation set:
- Cross Entropy Loss: 0.15
- Accuracy: 0.9541## Intended uses & limitations

For identifying common diseases in crops and assessing plant health. Not to be used as a replacement for an actual diagnosis from experts.## 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: 5e-5
- train_batch_size: 256
- eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 6### Framework versions

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