--- license: other tags: - generated_from_trainer datasets: - image_folder metrics: - accuracy model-index: - name: mobilenet_v2_1.0_224-plant-disease-identification results: - task: name: Image Classification type: image-classification dataset: name: New Plant Diseases Dataset type: image_folder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9541 --- # 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