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--- |
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datasets: |
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- rice |
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metrics: |
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- accuracy |
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model-index: |
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- name: rice_classification |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: rice |
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type: rice |
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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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- name: Accuracy |
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type: accuracy |
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value: 0.9768 |
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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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# image_classification |
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This model is a CNN model on the rice dataset to classify rice into 5 classes (Arborio, Basmati, Ipsala, Jasmine and Karacadag). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0116 |
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- Accuracy: 0.9768 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- optimizer: Adam |
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- num_epochs: 5 |
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### Training results |
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| Epoch | Loss | Accuracy | |
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|:-----:|:------:|:--------:| |
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| 1.0 | 0.0510 | 0.9363 | |
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| 2.0 | 0.0099 | 0.9695 | |
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| 3.0 | 0.5962 | 0.9767 | |
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| 4.0 | 0.4232 | 0.9828 | |
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| 5.0 | 0.0011 | 0.9859 | |
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