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--- |
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license: other |
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tags: |
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- generated_from_trainer |
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datasets: |
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- image_folder |
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metrics: |
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- accuracy |
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base_model: google/mobilenet_v2_1.0_224 |
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model-index: |
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- name: mobilenet_v2_1.0_224-plant-disease-identification |
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results: |
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- task: |
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type: image-classification |
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name: Image Classification |
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dataset: |
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name: New Plant Diseases Dataset |
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type: image_folder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- type: accuracy |
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value: 0.9541 |
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name: Accuracy |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# mobilenet_v2_1.0_224-plant-disease-identification |
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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). |
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It achieves the following results on the evaluation set: |
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- Cross Entropy Loss: 0.15 |
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- Accuracy: 0.9541 |
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## Intended uses & limitations |
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For identifying common diseases in crops and assessing plant health. Not to be used as a replacement for an actual diagnosis from experts. |
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## Training and evaluation data |
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The plant village dataset consists of 38 classes of diseases in common crops (including healthy/normal crops). |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-5 |
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- train_batch_size: 256 |
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- eval_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.2 |
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- num_epochs: 6 |
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### Framework versions |
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- Transformers 4.27.3 |
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- Pytorch 1.13.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.2 |
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