Instructions to use onnx-community/mobilenet_v2_1.0_224-plant-disease-identification-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use onnx-community/mobilenet_v2_1.0_224-plant-disease-identification-ONNX with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-classification', 'onnx-community/mobilenet_v2_1.0_224-plant-disease-identification-ONNX');
mobilenet_v2_1.0_224-plant-disease-identification (ONNX)
This is an ONNX version of linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification. It was automatically converted and uploaded using this Hugging Face Space.
Usage with Transformers.js
See the pipeline documentation for image-classification: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.ImageClassificationPipeline
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
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Evaluation results
- Accuracy on New Plant Diseases Datasetself-reported0.954