Image Classification
Transformers
PyTorch
TensorBoard
mobilenet_v2
Generated from Trainer
Eval Results (legacy)
Instructions to use ombhojane/healthyPlantsModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ombhojane/healthyPlantsModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ombhojane/healthyPlantsModel") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ombhojane/healthyPlantsModel") model = AutoModelForImageClassification.from_pretrained("ombhojane/healthyPlantsModel") - Notebooks
- Google Colab
- Kaggle
healthy-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
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Evaluation results
- Accuracy on New Plant Diseases Datasetself-reported0.954